This article was downloaded by: [Universite Laval] On: 10 July 2014, At: 06:40 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Enterprise Information Systems Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/teis20 Research on e-commerce transaction networks using multi-agent modelling and open application programming interface Chunhui Piao a b , Xufang Han c & Harris Wu d a School of Economics and Management, Shijiazhuang Tiedao University , Shijiazhuang, 050043, China b School of Information, Renmin University , Beijing, 100872, China c School of Information Science and Technology, Shijiazhuang Tiedao University , Shijiazhuang, 050043, China d Old Dominion University , Norfolk, VA, 23529, USA Published online: 09 Aug 2010. To cite this article: Chunhui Piao , Xufang Han & Harris Wu (2010) Research on e-commerce transaction networks using multi-agent modelling and open application programming interface, Enterprise Information Systems, 4:3, 329-353, DOI: 10.1080/17517575.2010.502975 To link to this article: http://dx.doi.org/10.1080/17517575.2010.502975 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
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This article was downloaded by: [Universite Laval]On: 10 July 2014, At: 06:40Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Enterprise Information SystemsPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/teis20
Research on e-commerce transactionnetworks using multi-agent modellingand open application programminginterfaceChunhui Piao a b , Xufang Han c & Harris Wu da School of Economics and Management, Shijiazhuang TiedaoUniversity , Shijiazhuang, 050043, Chinab School of Information, Renmin University , Beijing, 100872,Chinac School of Information Science and Technology, ShijiazhuangTiedao University , Shijiazhuang, 050043, Chinad Old Dominion University , Norfolk, VA, 23529, USAPublished online: 09 Aug 2010.
To cite this article: Chunhui Piao , Xufang Han & Harris Wu (2010) Research on e-commercetransaction networks using multi-agent modelling and open application programming interface,Enterprise Information Systems, 4:3, 329-353, DOI: 10.1080/17517575.2010.502975
To link to this article: http://dx.doi.org/10.1080/17517575.2010.502975
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.
This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Research on e-commerce transaction networks using multi-agent
modelling and open application programming interface
Chunhui Piaoa,b*, Xufang Hanc and Harris Wud
aSchool of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang 050043,China; bSchool of Information, Renmin University, Beijing 100872, China; cSchool of
Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043,China; dOld Dominion University, Norfolk, VA 23529, USA
(Received 20 July 2009; final version received 17 June 2010)
We provide a formal definition of an e-commerce transaction network. Agent-basedmodelling is used to simulate e-commerce transaction networks. For real-worldanalysis, we studied the open application programming interfaces (APIs) from eBayandTaobaoe-commercewebsitesandcapturedreal transactiondata.Pajek isused tovisualise the agent relationships in the transaction network. We derived one-modenetworks from the transaction network and analysed them using degree andbetweenness centrality. Integrating multi-agent modelling, open APIs and socialnetwork analysis, we propose a new way to study large-scale e-commerce systems.
Keywords: complex adaptive system; e-commerce transaction network; multi-agent modelling and simulation; open API; 2-mode network; social networkanalysis; systems science; systems theory; cybernetics
1. Introduction
With the popularity of the Internet, more and more enterprises and individuals areinvolved in e-commerce. Large numbers of buyers and sellers interact with eachother through transactions on e-commerce websites (Wang and Archer 2007). Thesebuyers and sellers’ future transaction behaviour are based on their past and currentinteractions. They can be viewed as adaptive buyer and seller agents. The transactionnetwork resulting from complex interactions between buyers and sellers in ane-commerce system is a complex adaptive system (CAS). It is infeasible to study thebehaviour of CASs using closed-form equations. The modelling and simulationapproach, often complemented by real-world data and calibration, is an importantway to study CASs (Li et al. 2002).
As indicated by Xu, systems science and the ideas behind it have penetrated manydisciplines including information systems, and systems theory has been considered asthe basis for information systems (Xu 2000). Current knowledge and theory aboutCAS mainly originates from cybernetics. In cybernetics, complexity science attemptsto study the nature of complex systems. CAS is one of the focuses of the complexityscience in cybernetics. Based on CAS theory, this article focuses on the analysis ofe-commerce transaction network using both multi-agent simulation and real-world
data captured through open application programming interface (APIs). Thetransaction network is divided into three 2-mode networks, namely, buyer-seller,seller-product and buyer-product networks. Each of the 2-mode networks is furthertransformed into 1-mode networks for degree and betweenness centrality analysis.The results show that the network analysis methods are effective in discoveringvaluable implicit knowledge from e-commerce transaction networks, which can beused to provide various value-added services for its users.
The rest of this article is organised as follows: Section 2 gives a brief review ofCASs, multi-agent modelling and simulation, social network analysis (SNA) andsocial networking services (SNSs) in e-commerce. Section 3 gives a formal definition ofan e-commerce transaction network and establishes a multi-agent model. Section 4provides a visual analysis of the simulated transaction network, and centrality analysesfor the seller, buyer and product networks. Section 5 introduces current open APIs ofeBay and Taobao. Section 6 describes the eBay transaction network data extractionprocess, and the design of Taobao data extraction. Section 7 analyses the eBaytransaction data. We conclude our article and present future directions in Section 8.
2. Related work
2.1. Adaptive features of e-commerce system
Systems science is necessary to address the overwhelming system complexity in theInternet era (Xu 2000, Warfield 2007). While adaptive control methods can be usedto research and solve the control problems of time-varying systems (Tang et al. 2000,Liu et al. 2009), CAS theory provides a new angle for recognising, understanding,controlling and managing complex systems. In a CAS, the members or so-calledagents are adaptive. The agents can communicate with each other and theenvironment, learn and accumulate experience, and change their structure andbehaviour (Tan et al. 2005). The core idea of CAS theory is that adaptation buildscomplexity (Ni et al. 2006).
Examples of CAS are often drawn from biology, sociology and economics(Tesfatsion 2003). The Web continues to grow at a phenomenal rate, and the amountof information on the Web is overwhelming. The CAS perspective has been appliedto studies of dynamic Web content organisation (Wu and Gordon 2004, Maya et al.2008) and Wikipedia (Ma and Xia 2008). E-commerce transaction system has thekey features of CAS: (1) e-commerce system is a dynamic network of many agentssuch as sellers and buyers acting in parallel. (2) The sellers and buyers are adaptive inthat they have the capacity to change and learn from transaction experience. (3) Anagent’s transaction behaviour depends on many factors such as productcharacteristics or the credit of the available sellers. (4) The structure of ane-commerce transaction network emerges from complex interactions. Thus, ane-commerce transaction system is a CAS.
2.2. Multi-agent modelling and simulation
A software agent is a software program that does something, often on behalf of aperson. Agents can retrieve and extract information from the Web with user’sguidance (Feng et al. 2003, Wu et al. 2004). Agents with domain knowledge canassist user with query formation (Tang et al. 2001), query expansion (Gong et al.2010) and document organisation (Wu et al. 2007). Independent agents can interact
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with one another in supply chains (Zhang and Bhattacharyya 2007), resourcemanagement (Feng et al. 2003) or to form dynamic services on an agent grid (Luoet al. 2007).
The basic idea of multi-agent modelling and simulation is that the complexsystem can be divided into corresponding agents simulating the real world, and theoverall behaviour of the system can be derived from investigating microscopic(individual) behaviour (Li and Wang 2007). Human beings make most importantdecisions in various stages of e-commerce transactions (Badica et al. 2005). Tradenetworks and labour markets have been studied in laboratory (McFadzean et al.2001). For a review of the main research problems in multi-agent modelling andsimulation, please refer Ni et al. (2006). A popular multi-agent simulation platformis Repast, a simulation toolkit developed by the Social Science Research ComputingLab at the University of Chicago (Jiang et al. 2006, Railsback et al. 2006).
This article applies the multi-agent modelling approach to the field ofe-commerce. A multi-agent model for an e-commerce transaction network isestablished and implemented using Repast. Based on the behaviour rules defined foreach type of agents, an e-commerce transaction network is generated by simulation.A network visualisation tool is then used to analyse the transaction network fromdifferent perspectives.
2.3. Social network analysis
A social network is a collection of people who are bound together through socialrelations. SNA is a quantitative method used to study the relationships among actorsin a social network. The SNA method analyses a social network from differentperspectives including centrality, cohesive subgroups and core-peripheral structures.Among them ‘centrality’ is often the central point of SNA, which shows theimportance of an actor in a network. Degree centrality is defined as the number oflinks incident upon a node. Betweenness, another important attribute of a node,reflects the intermediary location of a node linking other nodes (Marsden 2002). Thebetweenness has a capacity to facilitate or limit interaction between the nodes it links(Zhu and Li 2008).
SNA was given wide attention in the fields of Economics, Management andInformation Systems. For example, SNA is used to analyse the knowledge flow in anorganisation and identified the factors leading to knowledge sharing (Zhong andWang 2008). SNA can be used to create a collaborative recommendation systemsthat could suggest collusion risks associated with an account (Wang and Chiu 2008).SNA is used for hyperlink and click-stream analysis (Gordon et al. 2003, Wu et al.2003, 2006). Closely related to the topic of this article, Cai et al. (2008) developed asocial network model for a mobile e-commerce system, which could give ratingsbetween buyers and sellers in terms of their social relationship, actual geographicaldistances and transaction records.
1-mode networks consist of a single type of node. 2-mode networks consist oftwo types of nodes, such as customers buying products (Zhang 2006). Most of thetechniques to analyse 1-mode networks cannot be directly applied to 2-modenetworks. A 2-mode network can be transformed into two 1-mode networks whichthen can be analysed separately with standard techniques. A 2-mode network is astructure (U, V, R, w), where U and V are disjoint sets of vertices (nodes, unites),R�U 6 V is a relation and w:R!IR is a weight. A 2-mode network can be viewed
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as an ordinary (1-mode) network on the vertex set U þ V, divided into two sets Uand V, where the arcs can only go from U to V (Zaversnik et al. 2002). We cantransform 2-mode network into two 1-mode networks (U, RRT) and (V, RRT), whichcan be analysed separately with standard techniques.
In e-commerce, the transaction network can be divided into three 2-modenetworks: seller-product, buyer-product and seller–buyer networks. SNA andnetwork visualisation tools can be used to analyse these networks in depth.
2.4. Social networking services in e-commerce
An SNS builds social networks among people who share interests or activities (Boydand Ellison 2007). An e-commerce website can be viewed as a community whichgathers a large number of sellers and buyers. Many e-commerce websites such asAlibaba, Taobao, Amazon and eBay provide SNSs. Providing social networkservices on an e-commerce platform has become the most effective way of increasingcustom loyalty of the website.
Electronic marketplaces are calling for higher business interoperability (Guo2007, 2009). As the most famous electronic marketplace in China, Alibaba providesSNS in addition to its existing functions such as credit and payment service.‘Connections Hand’, an SNS introduced by Alibaba on 17 June 2009, aims atenhancing the online connections between traders. It provides traders with businessconnections discovery, customer relationship management and personalised businesscards (http://renmai.china.alibaba.com/).
‘Taojianghu’ (http://jianghu.taobao.com), an SNS product of Taobao, enablesthe sellers and buyers of Taobao to share more information and enjoy better services.Taojianghu can help the user keep contacts with friends, colleagues, classmates,family or enthusiasts and obtain more reliable trading experiences and suggestions.Besides the common functionalities of social network services such as ‘Add Friend’,Taojianghu provides functionalities characteristic of e-commerce websites including‘Taobao Faction’, ‘Help Me Select Item’, ‘Taobao Experience’ and ‘Favorite’.Taobao Faction provides a mechanism to organise the users with similar preferencesto form a faction, which promotes the communication among users with similarpurposes and demands. ‘Help Me Select Item’ enables a user to select items for his/her friends or other people, publish what he/her wants to buy, and let other usershelp select the demanded item or give suggestions. ‘Taobao Experience’ enables usersto publish personal experience about his/her interested items, and share it withfriends. ‘Favorite’ functionality helps a user collect his/her favorite storefront links,find favorites of friends, review friends’ favorites, and allow friends to review theirfavorites.
The Amazon SNS functionalities include ‘Your Profile’, and ‘Your Commu-nities’ (http://www.amazon.com). Using the ‘Your Profile’ functionality, users canadd friends, share favorites or present items bought recently. ‘Your Communities’enables users to join interested communities and exchange trading experience.
eBay rolled out social networking features including ‘Neighborhoods’, ‘MyWorld’ and ‘Groups’ (http://www.eBay.com). eBay ‘Neighborhoods’ is a place formembers to share information about the things they love, and interact with otherpeople who share their interests. ‘My World’ allows an eBay member to tell othermembers all about himself/herself, or to promote what the member sells. Uponentering the eBay My World page, others can see the member’s feedback profile,
items to be sold, find a link to the member’s storefront, and add the member to theirfavorite seller lists. When the feedback score of an eBay member is above 50, themember can create an interest group bringing together eBay members who sharecommon interests.
3. Multi-agent modelling and simulation of e-commerce transaction network
3.1. Definition of an e-commerce transaction network
As mentioned above, an e-commerce system is a CAS. An e-commerce transactionnetwork includes the relationships between buyers and sellers, buyers and products,sellers and products. A formal definition of e-commerce transaction network is givenbelow:
Definition: An e-commerce transaction network is a 4-tuple ECTN ¼ (S, B, P,T):
(1) S ¼ {s1, s2,. . ., sn} is the seller set. Each seller has an attribute set, si ¼ (Sid,SCredit, List of Products), i 2 [1, n], where Sid is the ID of seller si; SCredit isthe credit of seller si; List of Products ¼ (p1, p2,. . ., pu), pv 2 P, v 2 [1, u], is thelist of products sold by seller si.
(2) B ¼ {b1, b2,. . ., bm) is the buyer set. Each buyer has an attribute set,bi ¼ (Bid, BCredit, Preferences), i 2 [1, m], where Bid is the ID of buyer bi;BCredit is the credit of buyerbi; Preferences s ¼ { 5 Pid1, Pre1 4 , . . . 5Pidw, Prew 4 }, where Pidl is the ID of a product, Prel is the degree ofpreference for Pidl by buyerbi.
(3) P ¼ {p1, p2, p3 . . . pk} is the product set. Each product has an attribute set,pi ¼ (Pid, Price), i 2 [1, k], where Pid is the ID of product pi, Price is the priceof product pi.
(4) T ¼ {t1, t2, t3 . . . tr} is the transaction set. Each transaction has an attributeset, ti ¼ {Tid, Pid, Bid, Sid, Count, Amount, Evab2s, Evab2b), i 2 [1, r],where Tid is the ID of transaction ti; Pid, Bid, Sid are the product ID, buyerID and seller ID related to transaction ti; Count is the number of productssold in the transaction; Amount is the transaction amount; Evab2s andEvas2b are the feedback ratings given by the buyer and the seller,respectively.
3.2. Definition of transaction rules and transaction processes
In an e-commerce environment, buyers often select products and sellers. However insome situations, such as in biddings, sellers can select buyers to sell products. Thebehaviour rules and transaction processes for these two types of transactions aremodelled in a multi-agent simulation as follows.
(1) Transactions initiated by the buyer. A buyer is randomly selected as the trader who starts a transaction.. A product is randomly selected from the product set. The probability of
choosing a product is influenced by the buyer’s preference for the product.The more the buyer prefers it the more it is likely to be chosen.
. A seller is selected from the seller set. The higher the credit of a seller is, thehigher of the probability of the seller is to be chosen.
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. If a seller was selected then the following steps are performed:(i) The transaction-related information is stored in the transaction
history records. The relationship among the buyer, the seller andthe product is established.
(ii) Update the buyer’s preference for the product according to apredefined formula.
(iii) Generate the feedback ratings of both the seller and the buyer for thistransaction based on the predefined probability distribution.
(iv) Update the credit of both the seller and the buyer according to apredefined algorithm.
(2) Transactions initiated by the seller. A seller is randomly selected as the trader who starts a transaction.. A product is randomly selected from the products sold by the seller.. A buyer is selected from the buyer set. The probability of choosing a buyer is
influenced by the buyer’s preference for the product. Also, the higher the creditof a buyer is, the higher of the probability of the buyer is to be chosen.
. If a buyer was selected then the following steps are performed.(i) The transaction-related information is stored in the transaction
history records. The relationship among the buyer, the seller andthe product is established.
(ii) Update the buyer’s preference for the product according to apredefined formula.
(iii) Generate the feedback ratings of both the seller and the buyer for thistransaction respectively based on a predefined probabilitydistribution.
(iv) Update the credit of the seller and buyer according to a predefinedalgorithm.
3.3. Generation of an e-commerce transaction network
Based on the definitions of the e-commerce transaction network, rules and processes,we established and implemented a simulation model using Repast Simphony 1.1.0.Three types of transaction agents are defined in the model: seller-agents, buyer-agents, product-agents. ‘Network’ projection (Perez and Dragicevic 2009) is used todefine the relationships between the agents. The agents are initialised as follows:
(1) The number of products is set to 50. The price of each product is randomlyset from 1 to 1000.
(2) The number of sellers is set to 20. The initial credit of each seller is set to 2.The number of product types for a seller is randomly set to 1–5. Then, thespecific products sold by the seller are randomly selected from the productset.
(3) The number of buyers is set to 50. The initial credit of each buyer is set to 2.The number of preferred product types for a buyer is randomly set to 0–3.Then, the specific preferred products of the buyer are randomly selected from theproduct set. And the buyer’s preference for a product is randomly set to 1–3.
In Repast, the simulation clock is ‘Tick’. Every buyer and seller plays a gamewhere their current play depends on their strategies and previous play results.
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Figures 1 and 2 are two transaction networks generated using Repast. The yellowcrosses are products, red circles are buyers and blue squares are sellers. Edgesbetween sellers and products indicate that sellers sell products, and edges betweenbuyers and products indicate that buyers purchase products. Figure 1 is thetransaction network at tick 10 and the number of transactions is 136. Figure 2 is thetransaction network at tick 30 and the number of transactions is 1262.
4. Analysis of the simulated transaction network
4.1. Analysis of 2-mode networks in transaction network
Pajek, a network visualisation tool, is used to analyse each of the 2-mode networks:seller-product, buyer-product and seller–buyer networks. The e-commerce transac-tion network generated at tick 10 can be analysed as follows.
4.1.1. Analysis of seller-product network
In Figure 3, an edge between a seller node and a product node indicates that theseller sells the product. Edges are weighted by the number of products sold by theseller, represented by the thickness of the line. We can tell which sellers sell moretypes of products, which seller has sold largest number of products, and whichproducts are popular. In Figure 3, most sellers sell p15. Among the products sold bys14, p10 is the most popular product.
4.1.2. Analysis of buyer-product network
In Figure 4, an edge between a buyer node and a product node indicates that thebuyer purchases the product. Edges are weighted by the number of the productspurchased by the buyer, represented by the thickness of the line. We can tell whichbuyer has purchased the largest number of products and which products are most
Figure 1. Transaction network generated at tick 10.
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popular. In Figure 4, p15 was purchased by most buyers, and p34 is b49’s favouriteproduct.
4.1.3. Analysis of seller–buyer network
In Figure 5, an edge between a seller node and a buyer node indicates that the buyerpurchases products from the seller. Edges are weighted by the number of
Figure 2. Transaction network generated at tick 30.
Figure 3. Seller-product network.
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transactions between the buyer and seller, represented by the thickness of the line.We can tell which sellers have more trading partners and which buyers will be VIPcustomers of a specific seller. In Figure 5, the seller s14 has most trading partners,and the buyer b29 has most trading partners.
4.2. Applying social network analysis to e-commerce transaction network
The indirect relationships among the same type of agents in a transaction networkcannot be easily visualised in 2-mode networks. The three 2-mode networks can befurther transformed into six 1-mode networks, upon which we analyse the degreeand betweenness centrality.
4.2.1. Analysis of seller networks
Two seller networks are derived from the seller–buyer and seller-product networks,respectively, as shown in Figures 6 and 7.
By analysing the degree centrality of the seller networks, we can study thecompetition faced by each seller from customer and product perspectives. Degreecentrality of a node refers to the number of edges attached to the node. In Figure 6,an edge between two sellers denotes that they have at least one customer in common.Node s18 has the highest degree centrality, which indicates that s18 has mostcompetitors who share the same customers. In Figure 7, an edge between two sellersdenotes that they sell at least one kind of products in common. Node s18 has thehighest degree centrality, which indicates that s18 has the most competitors who sellthe same products.
The concept of betweenness centrality is used to further analyse the derived1-mode seller networks. The area of a node indicates the betweenness centrality of
Figure 4. Buyer-product network.
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the node. Nodes with higher betweenness centrality are more crucial to thetransmission of information through the network. As shown in Figures 6 and 7, s18has the highest betweenness centrality. If s18 fails to pass on information, the sellernetwork will be divided into two parts. Therefore s18 plays a crucial role in thenetwork. The betweenness centrality of s2 is 0 in Figure 6, but its betweennesscentrality is very high in Figure 7. In Figure 6, the connectivity of the buyer-connected seller network will not be influenced if node s2 is cancelled. In Figure 7,however, seven different sellers sell same products as s2, and the connection degreebetween s15 and other nodes will be decreased if node s2 is cancelled.
4.2.2. Analysis of buyer networks
Two buyer networks are derived from the seller–buyer and buyer-product networks,respectively, as shown in Figures 8 and 9.
By analysing the degree centrality of buyer networks, we can tell the potentialcollaborative partners of each buyer from product and supplier perspectives. InFigure 8, an edge between two buyers denotes that there is at least one seller who has
Figure 5. Seller–buyer network.
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sold product(s) to both of them. Node b33 has the highest degree centrality, whichindicates that b33 has most potential collaborative partners. In Figure 9, the edgebetween two buyers denotes that they buy at least one kind of products in common.Nodes b33 and b29 have the highest degree centrality, which means that b33 and b29have the most partners with similar product preferences.
Betweenness centrality is used to further analyse the derived 1-mode buyernetworks. As shown in Figures 8 and 9, b33 and b46 have the highest betweennesscentrality respectively, hence they are crucial to the transmission of informationthrough the buyer networks. Node b12 in Figure 8 is important to thecommunication among b10, b34, b46 and other nodes. So the betweenness centralityof b12 is high. However, if b12 in Figure 9 fails to pass on information, the othernodes such as b17, b2 and b6 may fulfil this role and the communication chain is stillintact, therefore the betweenness centrality of b12 is 0. It means that b12 has nointermediary effect on connecting other buyers based on the products that b12bought.
Figure 6. Seller network derived from seller–buyer network.
Figure 7. Seller network derived from seller-product network.
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4.2.3. Analysis of product networks
Two product networks are derived from the buyer-product and the seller-productnetworks, as shown in Figures 10 and 11, respectively.
By analysing the degree centrality of product networks, we can tell the popularityof each product from buyer and seller perspectives. In Figure 10, an edge between twoproducts denotes that there is at least one buyer who has bought both of the twoproducts. In Figure 10, the degree centrality of p15 is 2, which indicates that there areat least two different buyers who have bought p15. Nodes p10, p34, p49 are isolatednodes, which indicate that the buyers who bought one of the three kinds of productshave not bought any other kind of products. In Figure 11, an edge between twoproducts denotes that there is at least one seller who has sold both of the twoproducts. Node p15 has the highest degree centrality, which indicates that the numberof sellers who have sold p15 and at least one other kind of products is greater thanthat of sellers who have sold two or more kinds of products excluding p15.
Betweenness centrality is used to further analyse the derived 1-mode productnetworks. As shown in Figures 10 and 11, p15 has the highest betweenness centrality, soit is crucial to the transmission of information through the product network. In Figure10, p34 is an isolated node, which indicates that no buyer has bought it and it has noeffect on the connectivity of the network whether it is cancelled or not. However, inFigure 11, p34 is crucial to the connection among p40, p49 and other nodes.
5. Open API from eBay and Taobao
Web 2.0 is increasingly changing the way people learn, work, communicate, accessinformation and share knowledge (He et al. 2009, Wu and Cao 2009). Manywebsites, including the emerging SNS websites such as Facebook, Twitter and
Figure 8. Buyer network derived from seller–buyer network.
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Renren as well as the dominant Internet websites such as Google and Baidu, haveprovided open APIs to the third parties. Many mainstream e-commerce websites(e.g. Amazon, eBay, Taobao) provide various open APIs enabling third parties toextract data to create new applications.
5.1. eBay Developers Program and open APIs
eBay Developers Program was founded in 2000. Using this program, developers cancreate various eBay applications, tools or services using eBay open API. Third partysoftware applications now account for over 25% of eBay.com listings. eBay providesfive categories of open APIs: search, selling, buying, users and alerts. Each categoryincludes several specific APIs, as shown in Figure 12. Among them, shopping API,merchandising API and trading API can be used to develop 3rd-party applications(http://developer.ebay.com).
We constructed eBay transaction network based on the above APIs. In theShopping API, GetCategoryInfo function gets the sub-category IDs of a category;FindPopularItem function gets popular items of a brand; GetSingleItem functionobtains the ID of the seller selling specified item; GetUserProfile function gets a seller’sor buyer’s profile information. Trading API’s GetFeedback function gets a user’strading records and detailed feedback information. Based on the multi-agent model
Figure 9. Buyer network derived from buyer-product network.
established for e-commerce transaction network and real transaction data extractedfrom eBay.com, a real-world e-commerce transaction network can be constructed.
5.2. Taobao Open Platform and APIs
On 8 September 2008, Taobao.com announced the release of Taobao Open Platform(TOP). Open API is the core of TOP. As of January 2010, 25,000 grass-roots developershavebeenbrought intoTOP,more than220APIsareopened to thedevelopers, andabout4000 applications have been verified and released by Taobao.com. These applicationsinvolve various aspects in e-commerce, including item display, customer service, shop
Figure 10. Product network derived from buyer-product network.
Figure 11. Product network derived from seller-product network.
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marketing, backgroundmanagement and logisticsmanagement. The number of TaobaoAPI calls exceeds 200 million a day (http://open.taobao.com).
Taobao APIs are divided into four categories: Transaction, SNS, AppStore andImage Space (Figure 13). Transaction APIs can be used to get product information,query transaction records, add feedback and so on. SNS API can be used to get userprofile information in the community, find listings of friends, and determine whethertwo users are friends or not. AppStore API can be used to get the subscriptionrelationships between users and AppStore applications. Picture Space API can beused to upload, delete or search pictures.
Taobao grants different API access permissions to different applications. Adeveloper has to choose appropriate roles for an application. The application rolesavailable on TOP include: public query application, buyer application, sellerapplication, merchant application, senior application, community application, mediaapplication, Taobaoke application, professional application. Among them, someroles demand users to login first, while other roles can work without login. If anapplication with the buyer application role needs to access a seller’s transactionrecords, the application has to be granted permission by the user.
6. Extracting e-commerce data based on open APIs
6.1. eBay transaction data
6.1.1. Selecting transaction data
There are hundred of millions of registered users on eBay.com and millions oftransactions every day. We limited the data collection using the following method.
(1) Select 10 famous brands in the cell phones & smartphones category, whichare Apple iPhone, BlackBerry, CECT, HTC, Nokia, Samsung, SonyEricsson,LG, Motorola and Palm. For each brand, the top 10 most popular productsare selected into the set of items SItems.
(2) For each popular product in SItems, top-rated sellers are selected into the setof sellers SSellers.
(3) For each seller in SSellers, all the transaction records for the previous 12months are retrieved into the set of transactions STrans.
(4) For each seller in SSellers, the complete feedback profile and the feedbackscores accumulated in the previous 12 months are retrieved into the set ofseller feedback profile SSFeedback.
(5) The buyers who participated in the transactions in STrans form the set ofbuyers S Buyers.
(6) For each buyer in SBuyers, the complete feedback profile and the feedbackscores accumulated in the previous 12 months are retrieved into the buyerfeedback profile SBFeedback.
6.1.2. Data extraction
We extracted data on items, sellers, transactions and buyers:
(1) Extracting item data. Using Shopping API’s GetCategoryInfo function, get the ID of category
‘Cell Phones & PDAs’ and the ID of its sub-category ‘Cell Phones &Smartphones’;
Figure 13. Categories of TOP API.
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. Using Shopping API’s FindPopularItem function, extract the detailedinformation of 100 kinds of popular items in the ‘Cell Phones & PDAs’category of a specified brand, and put the information into an XMLdocument;
. Parse the XML document and find the items belonging to the category of‘Cell Phones & Smartphones’ and store the detailed information of theitems to corresponding table TItems in the database.
(2) Extracting seller IDsEach record in table TItems is processed as follows:. Get the item ID value from the record;. Using Shopping API’s GetSingleItem function, extract a seller ID and
store it into the table TSellers in the database.(3) Extracting transaction data
Each seller record in table TSellers is processed as follows:. Get the seller ID value from the record:
Using Trading API’s GetFeedback function, extract all the transactionrecords related to the specific seller and store them into the transactionstable in the database. The transaction record includes the detailedinformation about a trade and feedback information on the seller profile.Among the sellers selected, some are very active sellers on eBay. Tocomplete data extraction in a reasonable period of time, up to 1000transaction records are extracted for each seller.
(4) Extracting seller profile informationEach seller record in table TSellers is processed as follows:. Get the seller ID value from the record;. Using Shopping API’s GetUserProfile function, extract the profile
information of the seller, including store URL, seller items URL, aboutme URL, feedback score, feedback rating star, positive feedback percent,top-rated seller, etc., and save the data into TSellers.
(5) Extracting buyer profile informationEach transaction record in table TTrans is processed as follows:. Get the buyer ID from the record, and save it into the table TBuyers;. Using Shopping API’s GetUserProfile function, extract the profile
information of the buyer, including registration date, feedback score,feedback rating star, positive feedback percent, etc., and save the data toTBuyers.
6.1.3. Pre-processing of transaction data
Using above methodology, we extracted the transaction-related data on 6 February2010. First we extracted the data of the popular items in the category of ‘Cell Phones& PDAs’ in XML format, and stored the data into database; then we obtained 311data records of the popular items in the category of ‘Cell Phones & Smartphones’.Starting from the 311 items data records, we finally extracted 98,599 transactionsinvolving 141 sellers and 87,661 buyers.
In the transactions table, we found a large number of records with null item_title value. After analysing the data, we find that eBay.com only allows people toaccess the detailed transaction-related data within the past 90 days. To makeanalysis of the items sale, the transaction data is further separated into two parts,
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one includes all the transaction data within past 90 days, and another includesthe transaction data over 90 days ago. The results of the separation are shown inTable 1.
6.2. Extracting Taobao social networking data
At present, TOP provides 22 API functions for SNSs. The functions related tofavourites or friends include:
(1) taobao.jianghu.user.getProfile, which can be used to obtain the profileinformation of the user joining Taojianghu community, including user ID,nick, constellation, sex, favourite, etc. The favourite of the user can bedescribed from different aspects such as recently requested music, games,books, brands, etc;
(2) taobao.jianghu.users.getProfileList, which can be used to get the profileinformation of specified users not including favourites information;
(3) taobao.jianghu.friends.getFriendList, which can be used to search for thefriends of a user;
(4) taobao.jianghu.friends.areFriends, which can be used to judge whether twousers are friends or not.
All Taobao SNS API functions are now restricted to the Taojianghu community.The four SNS API functions described above are classified into a private category,which requires the user to sign into Taobao.com first.
We design the extract process as the following:
(1) Using the taobao.trades.sold.get function of Item API under transactioncategory, extract the transaction records of the seller (current user), each ofwhich is composed of the following data items: buyer nickname, trading time,item ID, price, amount and buyer feedback.
(2) Using taobao.users.get function of User API under transaction category, andusing a buyer nick obtained at step (1) as the input parameter value of thefunction, get a specific buyer’s detailed public information including user ID,sex, buyer-credit, seller-credit, and location.
(3) Using SNS API’s taobao.jianghu.friends.areFriends function, and using abuyer’s user ID obtained at step (2) as the input parameter, determinewhether the buyer is also a friend of the seller (current user) or not.
(4) Using SNS API’s taobao.jianghu.user.getProfile function, extract theprofile information of each friend-buyer of the seller (current user) inTaobao community including what the buyer recently wanted, preferredbrands, etc.
Table 1. Results of separating transaction data.
Number of transactions Number of sellers Number of buyers
Total 98,599 141 87,661Within 90 days 62,010 130 55,284Before 90 days 36,589 103 33,366
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(5) Using SNS API’s taobao.items.all.get function, extract the information of allthe items sold by the user (current user) including category ID, title, etc.
(6) Using taobao.items.get function of Item API under transaction category, andusing an item category ID obtained at step 5 as the input parameter, get thenicknames of all the sellers (competitors) selling the same kind of item.
(7) Using the taobao.users.get function of User API under transaction category,and using the nicknames of the competitors obtained at step (7) as the inputparameter, extract their public information.
7. Analysis of eBay transaction network
7.1. Reduced transaction data set for Nokia transactions
The extracted 90-day transaction data involves a large number of transactions(62,010), buyers (55,284) and items (29,739). To perform a focused analysis, we haveto form a smaller transaction data set by performing fuzzy matching between thethree words (‘Nokia’, ‘NOKIA’ and ‘nokia’) and the item names of the transactionrecords. Through this data filtering method, we obtained 2282 Nokia relatedtransaction records involving 53 sellers, 2068 buyers and 1266 items. Note that thesame item sold by different sellers have different item IDs and potentially differentitem names, and therefore treated as different items in our analysis. This is thelimitation of our study.
7.2. Analysis of the 2-mode networks
7.2.1. Seller–buyer network on eBay.com
Out of 2282 transaction records related to Nokia, there are 1993 pairs of sellers andbuyers who individually have conducted one transaction, while 104 pairs of sellersand buyers conducted two or more transactions. To focus on the seller–buyerrelationships conducting transactions repeatedly, we only pay attention to therelationships between the 104 pairs of sellers and buyers, involving 289 transactionrecords, 24 sellers, 103 buyers. The seller–buyer network is shown in Figure 14.
The overall transaction relationships between the buyers and sellers can be seenin Figure 14, such as which sellers have more trading partners and which buyers willbe VIP customers of a specific seller. For example, the sellers, freeluo888, phone-blowout-store and hellotofriend2008, have more trading partners than other sellers.Buyer magalingam and seller freeluo888 conducted 13 transactions, and buyermagalingam is a VIP trading partner.
7.2.2. Analysis of seller-item network on eBay.com
Out of the 2282 transaction records related to Nokia brand, there are 1005 items soldonly once, while 261 items were sold multiple times by the sellers. Focusing on thepopular items, we only pay attention to the transactions related to the 261 items,involving 1277 transaction records and 42 sellers. The seller-item network is shownin Figure 15.
The overall relationships between the sellers and items can be seen in Figure 15,such as which sellers are selling more kinds of items or have sold items more
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Figure
14.
Seller–buyer
network.
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Figure
15.
Seller-item
network.
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frequently, and which items have been sold more. For example, seller helloto-friend2008 is selling more variety of items than other sellers, and the item numbered260464188607 was sold more often than other items.
7.2.3. Analysis of buyer-item network on eBay.com
Out of the 2282 transaction records related to Nokia brand, 2228 items were boughtonly once, while 22 items were bought multiple times. Focusing on repeated buyingbehaviour, we only pay attention to the transactions related to the 22 items, whichinvolve 54 transactions and 20 sellers. The buyer-item network is shown in Figure 16.
The relationships between the buyers and items can be seen in Figure 16, such aswhich buyers have bought more items or have bought the items more frequently, andwhich items have been bought more often than other items. For example, the itemnumbered 250472609292 is bought more frequently by the buyers, and it is also themost preferred item by the buyer rbus7300.
8. Conclusions and future works
This article proposes a definition of an e-commerce transaction network. Using amulti-agent modelling approach, we simulated e-commerce transaction networksusing transaction rules. Three 2-mode networks, seller-product, buyer-product andseller–buyer networks, are obtained from the transaction network. Pajek, a networkvisualisation tool, is used to visualise the relationships among the agents in 2-modenetworks. After converting 2-mode networks into 1-mode networks, we conducted
Figure 16. Buyer-item network.
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degree and betweenness centrality analysis. By visualising the transaction networkwe gained valuable insights into an e-commerce system, such as the distribution ofpopular products, the competitive relationships among sellers, the buyer preferencesfor the products, and potential collaborative relationships among buyers. Theinsights can be used to recommend products or sellers to the buyers, or to providevarious value-added services for the sellers such as analysis of competitors andpotential VIP customers.
The open APIs of eBay and Taobao are studied and utilised in our researchwork. We obtained data for more than 60,000 eBay transactions related to mobilephones of 90 days until 6 February 2010. We also designed a process of extractingsocial networking data using the Trade API and SNS API on the TOP. Constructingand analysing the social network related to a seller, we get useful information topromote the seller’s items.
To further study e-commerce transaction networks, we intend to conductfollowing research in the near future:
(1) Design different solutions for data extraction and analysis based on eBay andTaobao APIs. Evaluate, select and process transaction information indifferent time periods related to the same product category. Then, analyse thecorresponding transaction networks from different perspectives.
(2) To verify the effectiveness of the simulation model, the transaction networksresulting from the evolvement process can be compared with the changingresults of the transaction network derived from the real e-commerce website.Based on the comparison results, we can improve the multi-agent simulationmodel by adjusting the behaviour rules of the agents.
(3) Further analyse the social networks in the communities on e-commerceplatform using their open APIs. Try to explore how to enhance therelationships among buyers who share the same preferences, and how toprovide personalised value-added services to different users.
(4) Research on personal information mining and recommendation algorithmsfor large-scale e-commerce systems based on the improved multi-agentsimulation model and real-world data.
Acknowledgement
This work is supported by the International S&T Cooperation Program of China under thegrant No. 2007DFA11110.
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