ith the rapid growth in the popularity ofportable computation and communicationdevices (e.g., mobile phones, PDAs, and lap-tops), a new networking environment where
mobile users can take advantage of opportunistic encounterswith other users to forward data or share information in apeer-to-peer fashion has been attracting increasing interest [1,2]. It has been demonstrated that opportunistic informationexchanges between mobile users are highly relevant to theirmobility patterns [1, 3]. Consequently, modeling humanmobility to reflect realistic mobility patterns plays an essentialrole in accurately evaluating and analyzing the performance ofprotocols and applications.
There are many factors that contribute to the people’s com-plex movement behaviors in their daily lives, including socialrelationships among individuals, various geographical circum-stances, and transport facilities. Social interaction is one ofthe key factors because individuals belong to social circles(e.g., their families, friends, and co-workers), and their socialties strongly affect their movement decisions. However, thereare few existing mobility models [4, 5] considering the socialaspects of human mobility.
In order to model realistic social dimensions of humanmotion, we need to deeply understand the structure of realsocial networks. Fortunately, social network theory [6], a use-ful and powerful tool for mathematically modeling the com-plex social relationships between people, has been investigatedin considerable detail in sociology and other areas for years.One of the most representative properties of social networksis the community structure [7, 8]. A community is a structuralsubunit (can be represented as a set of individuals) of a social
network. Individuals have many more social connections withother individuals inside their own community than with peo-ple from other communities. In addition, a recent study [8]demonstrates that there are significant overlaps (i.e., sharingof common individuals) between communities in many realsocial networks. Community structure has significant impacton people’s motion behaviors. For instance, individuals meeteach other more frequently in their own community than theymeet people from other communities [4].
Recently, heterogeneous human mobility popularity, whichis quite useful for designing efficient data forwarding schemes,has been observed from various real mobile user traces [1]. Ina society some people are more popular and have moreopportunities to meet others. These popular people are moresuitable for relaying data than unpopular ones. To the best ofthe authors’ knowledge, however, there has been no existingmobility model considering heterogeneous human popularityyet. To model realistic heterogeneous popularity rankings, weresort to social network theory.
In this article we demonstrate that the overlapping commu-nity structure of social networks is a key factor that con-tributes to heterogeneous human mobility popularity. Themain contribution of this article is to answer the followingquestions:• How does social network theory help us to model human
mobility?• Why does the overlapping community structure contribute
to human heterogeneous popularity?• How can we use the overlapping community structure to
model human heterogeneous motion popularity for a goodtrade-off between reality and complexity?
Shusen Yang, Imperial College LondonXinyu Yang and Chao Zhang, Xi’an Jiaotong University
Evangelos Spyrou, Imperial College London
AbstractHuman mobility modeling plays an essential role in accurately understanding theperformance of data forwarding protocols in mobile networks and has beenattracting increasing research interest in recent years. People’s movement behaviorsare strongly affected by their social interactions with each other, which, however,are not sufficiently considered in most existing mobility models. Recent studies insocial network theory have provided many theoretical and experimental results,which are useful and powerful for modeling the realistic social dimension of humanmobility. In this article we present a novel human mobility model based on hetero-geneous centrality and overlapping community structure in social networks. Insteadof extracting communities from artificially generated social graphs, our model man-ages to construct the k-clique overlapping community structure which satisfies thecommon statistical features observed from distinct real social networks. Thisapproach achieves a good trade-off between complexity and reality. The simula-tion results of our model exhibit characteristics observed from real human motiontraces, especially heterogeneous human mobility popularity, which has significantimpact on data forwarding schemes but has never been considered by existingmobility models.
Using Social Network Theory forModeling Human Mobility
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To answer the above questions, we briefly review social net-work theory and human mobility models, and then proposethe Heterogeneous Human Walk (HHW) model, whichexhibits most important characteristics of human mobility pat-terns observed from real traces, especially heterogeneoushuman popularity.
Description of Social Networks andCommunity StructureInteractions between individuals in a society form a social net-work with a complex topology structure that can be represent-ed as a directed or undirected graph [6]. For example, Fig. 1ashows a small undirected social graph of 18 individuals. Manysocial networks share common characteristics [9], includingthe “small world” effect, power-law degree distribution, andlarge clustering coefficient. Designing artificial social networkmodels to produce these common features is also a researcharea, especially in the statistical physics and applied mathe-matics communities. Excellent surveys of social and complexnetworks can be found in [6, 9].
A property that seems common in many social networks isthe community structure. The connections among individualswithin a community are dense, but between different commu-nities they are sparser. Thus, the social network structure canbe viewed at three levels: individual, community, and wholenetwork. A large number of community detection algorithmshave been proposed to divide a social network into separatedcommunities [10]. Figure 1b illustrates the four isolated com-munities of the social network in Fig. 1a.
However, most real social networks are characterized bywell defined statistics of overlapping communities to whicheach of us belongs, including those related to our scientificactivities and personal lives (e.g., school, hobbies, and family).Palla et al. [8] propose an algorithm to extract a k-clique over-lapping community structure from social networks. A k-cliqueis a complete subgraph of size k in a social network, and a k-clique community is a union of all k-cliques that can reachone another through a series of adjacent k-cliques (whereadjacency means sharing k – 1 nodes). For example, commu-nity 3 in Fig. 1c is constituted of two adjacent 4-cliques: thecomplete subgraphs are made up of four individuals {10, 11,12, 13} and {10, 11, 12, 14}, respectively. The two 4-cliquesshare the common individuals 10, 11, and 12.
For the overlapping community structure, each individual nin a social network can be characterized by a membershipnumber MNn, which is the number of communities to which
the individual belongs. In addition, any two communities xand y can share Sx,y
ov individuals, which is defined as the over-lap size between these two communities. Finally, the size Sx
com
of any community x can most naturally be defined as the num-ber of individuals that belong to it. For instance, as shown inFig. 1c, MN6 = 3, S3,4
ov = 2, and S1com = 6.
Quantitatively, denote the complementary cumulative dis-tribution functions (CCDFs) of membership number, overlapsize, and community size as P(MN), P(Sov), and P(Scom),respectively. For instance, P(Sov) means the proportion ofthose overlaps that are larger than Sov. Based on observationof various real social networks, Palla et al. [8] report thatP(MN), P(Sov), and P(Scom –(k – 1)) approximately followpower-law distribution P(x) ~x–τ, with the exponents τ =PRMN, τ = PROsize, and τ = PRCsize, respectively. They alsofind that both the values of PRMN and PROsize are not lessthan 2, and the value of PRCsize is between 1 and 1.6. OurHHW model will use these three statistical properties as therealistic assumptions to artificially construct k-clique overlap-ping community structure.
Description of Human Mobility PropertiesObserved from Real TracesThere are an increasing number of real data sets collectedfrom wireless LAN, Bluetooth, and GPS-based traces in cam-puses, conferences, and entertainment environments, as wellas other locations. These distinct traces demonstrate surpris-ing common characteristics. Many human mobility modelsuse these properties as their design foundations or the evi-dences to evaluate their reality. Since we focus on the socialdimension of human mobility, we only summarize someimportant characteristics that are relevant to this article asfollows:• As [1] mentioned, people differ in their popularity. A node
has a local popularity within its community and a globalpopularity across the whole network. Since a node maybelong to more than one community, it can also have multi-ple local popularities.
• It is shown that human traces exhibit a high degree of tem-poral and spatial regularity [11, 12]. People mostly movewithin a small number of confined locations and periodical-ly appear in these locations with high probability.
• The CCDF of inter-contact time (the time elapsed betweentwo successive contacts between the same pair of individu-als) consists of a power-law head followed by an exponen-tially decaying tail after a certain time [13].
Figure 1. An example of a) a social network; b) the separated community structure; c) the 4-clique overlapping community structure.
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A Review of Human Mobility ModelsWe briefly review recent representative human mobility mod-els in this section. For a recent complete and comprehensivedescription of human mobility models, we refer the reader toMusolesi et al. [14]. According to social awareness, we dividethe mobility models into two categories: real-trace-based mod-els and social-aware models.
Real-Trace-Based ModelsThe key underlying idea of these models is to capture individu-als’ physical motion features observed from real traces. Forinstance, the Levy Walk (LW) model [15] captures the specialstatistic distributions (e.g., pause time, velocity, and flightlength) observed from GPS-based traces. The synthetic tracesgeneration of the LW model is based on explicitly reproducingthese distributions using random variable generators. Anotherexample is the model proposed in Hsu et al. [12], which observestwo special mobility features (location preferences and periodi-cal reappearance) from wireless LAN-based traces and modelsthe two observed properties using a Markov-based approach.
In general, although most of these models manage toreproduce observed realistic human mobility patterns (mostlythe inter-contact time distribution) with a high degree of accu-racy, these models mainly focus on individual-level mobilitybehaviors and assume that each node moves independent ofthe others. Consequently, the lack of considering network-level social interactions among individuals undermines thereality of these mobility models.
Social-Aware Models
Community-Based Model — Musolesi et al. [4] propose amodel that uses social networks (generated by an artificialsocial network model or input by users) as the input. By usingthe community detection algorithm proposed by Newman etal. [7], the social network is divided into separated communi-ties, which are associated with special regions in the simula-tion area. Movements of individuals are driven according to apreferential social attractivity scheme. Since people have dif-ferent social roles at different times, this model periodicallychanges the underlying social graph.
Sociological Behavior-Based Models — The Sociological Inter-action Mobility for Population Simulation (SIMPS) [5] mobili-ty model derives the motion of users based on sociologicalresearch results. Individuals’ movements are governed by boththeir social relationships (based on a generated social graph)and geographically surrounding individuals. Although SIMPSmodels the individuals’ fine-grained motion behaviors well, itassumes that the underlying social network is fixed, and thecommunity structure is not considered.
Although both social-aware models try to find the social-dimension causes of human mobility patterns (inter-contacttime distribution), none of them considers how social networkstructure contributes to heterogeneous human popularity. Inaddition, both models are based on manually input or artifi-cially generated social graphs, which introduce the followingproblems. First, it is unsuitable to manually input a socialgraph for mobility modeling, because the mobility model canonly simulate very specific scenarios and requires a greatamount of effort for users when the simulated population islarge. Second, the social network generation models used inthe aforementioned social-aware mobility models are not real-istic enough. For instance, the Albert-Barabási scale freegraph [16] and random graph used in the SIMPS model can-not reflect many features of real social networks [9].
From Time-Varying Social Interactions toHeterogeneous Human Motion PopularityWe use one example to illustrate the cause of human hetero-geneous popularity and the spatio-temporal regularity of peo-ple’s movement. As shown in Fig. 2, the periodicallytime-varying social roles of people in a society during a day(could also be a week or other durations) contribute to differ-ent social networks composed of these people and differentcorresponding overlapping community structures at differentperiods. An individual often plays the same role and belongsto the same community or communities in the same period ofdifferent days.
Global Heterogeneous PopularityScientist n1 belongs to only one community in each period.Mapping from community to geographical location, it canappear in only one corresponding location during each period.However, salesman n2 is in the overlap of three communitiesduring 6:00–12:00 and 12:00–18:00, and could appear in any ofthe corresponding locations. Therefore, n2 has the opportunityto meet people in multiple communities, while n1 could onlymeet people in one community. Consequently, n2 has higherglobal popularity than that of n1.
Local Heterogeneous PopularityBesides their global popularities across the whole social net-work, people also have local popularities in their communities[1]. From the viewpoint of graph theory, each node has a localdegree in the community to which it belongs (the number ofneighbors of a node in a community). For instance, in Fig. 1cthe local degree of node 17 in community 4 is 6. Similar tomapping from overlapping community structure to global pop-ularity (illustrated in Fig. 2), an individual’s local popularity ina community depends on its local degree in this community.
It is not necessary for a node with large global popularity tohave large local popularity and vice versa. This makes senseintuitively that an assistant in an academic department usuallyhas more opportunities to meet people in this departmentthan a postman would have. Take Fig. 1c, for example:although node 6 belongs to three communities and thus has ahigher global popularity than node 17, which only belongs toone community, its local popularity in community 4 is lessthan that of node 17.
The HHW ModelOverviewThe main aim of the HHW model is to generate realistichuman mobility patterns,, especially heterogeneous humanpopularity based on the observation of real social networks(i.e., the realistic assumptions of an overlapping communitystructure).
To obtain a realistic overlapping community structure, anintuitional approach could be to first input or artificially gen-erate a social graph by using a social network model similar toexisting social-aware mobility models [4, 5]. Having obtained asocial graph, the algorithm proposed in [8] can be used todetect overlapping communities for this social graph. Thisapproach, however, will significantly increase the computa-tional and implementation complexity of social graph con-struction and overlapping community detection.
To achieve a trade-off between reality and complexity, theHHW model directly constructs synthetic overlapping commu-nities rather than detecting them from input or generatedsocial graphs, using the realistic assumptions of P(MN),P(Sov), and P(Scom) as input. The HHW model is composed of
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three components: establishment of overlapping communitystructure and heterogeneous local degree, mapping communi-ties into geographical zones, and driving individual motion.We detail these processes in the following subsections.
Establishing k-clique Overlapping Communities andHeterogeneous Local DegreeA day (or a week) is divided into several periods, and theoverlapping community structures are different in differentperiods of a day but are the same in the same period of differ-ent days. For readability, a node whose membership numberis larger than 2, equals 2, or equals 1 is called M-3 node, M-2node, or M-1 node, respectively. The synthetic overlappingcommunity structure for each period of a day is constructed inthe following four steps:• First, simultaneously assign each node’s membership num-
ber, which follows P(MN) with exponent PRMN, and estab-lish initial empty communities whose sizes Scom followP(Scom – (k – 1)) with exponent PRCsize.
• Second, use all M-3 nodes to establish initial overlapsbetween pairs of initial generated communities.
• Third, modify initial overlaps by allocating all M-2 nodes toguarantee that overlaps’ sizes follow P(Sov) with the expo-nent PROsize.
• Fourth, allocate all M-1 nodes to the established communi-ties.
Initial Empty Community and Membership Number Establish-ment — Denote the sum of the generated membership num-bers of all nodes as S1 and the sum of the sizes of all initial
generated communities as S2. According to the definition of anode’s membership number, S1 should be equal to S2. Basedon this constraint, the generation procedure is as follows:Step 1 For each node n, generate a random variable (RV)
that follows P(MN) with exponent PRMN, and then set thisRV as MNn.
Step 2 Set i ←1 and S2 ← 0.Step 3 Generate an RV RVi which follows P(Scom –(k–1))
with exponent PRCsize (for the k-clique overlapping commu-nity structure, the potential size of the ith community is RVi+ k–1). Calculate S2 using Eq. 1. If S2 < S1, set i ← i + 1,go to step 3. If S2 > S1, abandon all generated RVs, thengo to step 2.
Step 4 If there is a node whose membership number is larg-er than i (each node’s membership number should not belarger than the number of established communities), aban-don all generated RVs and go to step 1 to regenerate eachnode’s membership number; otherwise, assign each RVgenerated in step 3 plus k – 1 as the size of each generatedcommunity; then the algorithm ends.
(1)
Constructing Initial Overlaps — For each M-3 node n, random-ly select MNn unsaturated communities. Since the sizes ofcommunities have been assigned, each selected communityshould not overflow after individual n joins it (e.g., community6 in Fig. 3 is already saturated and hence will not be selectedby the two nodes a and b). Then establish (MNn+1)MNn/2 ini-tial overlaps between each pair of these selected communities
S i k RVjj
i
21
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Figure 2. An example of the time-varying social roles, communities, and corresponding locations of scientist n1 and salesman n2: a) theroles of scientist n1 at different periods of a day; b) n1’s corresponding communities in each period; c) the corresponding locations ofeach community; d) the roles of salesman n2 at different periods of a day; e) three communities to which n2 belongs during 6:00–12:00;f) the corresponding locations of each community.
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(according to the definition of membership number, all theseselected MNn communities should be connected with eachother). Finally, node n joins the MNn selected communities,and the (MNn + 1)MNn/2 corresponding initial overlaps. Fig-ure 3 gives an example of the eight initial overlaps constructedby two M-3 nodes.
Satisfying Power-Law Overlap Size Distribution — Let Oxydenote the initial overlap between two communities x and y,Ix,y
ov denote the number of M-3 nodes in Oxy , and Rxcom and
Rycom denote the remaining available spaces (e.g., in Fig. 3 the
remaining available space of community 2 is 3) of two com-munities x and y, respectively. The following algorithm modi-fies the initial overlaps by assigning their sizes and meanwhileallocating generated M-2 nodes into overlaps.Step 1 If all initial overlaps are modified or all M-2 nodes
are allocated, the algorithm ends. Generate RVi that followsP(Sov) with the exponent PROsize. If RVi is larger than themaximum size of all communities, regenerate RVi.
Step 2 Check if there is an initial overlap Oxy that satisfiesIx,y
ov = RVi. If there is such an initial overlap, mark Oxy as amodified overlap, set i ← i + 1, and then go to step 1. Ifthere is no such initial overlap, find an overlap Oxy that sat-isfies (Ix,y
ov < RVi) ∧ (ΔN < Rxcom) ∧ (ΔN < Ry
com), where ΔN= RVi – Ix,y
ov. Then randomly select M-2 nodes with thenumber of ΔN and allocate them into Oxy, community x andcommunity y. Then mark Oxy as a modified overlap, set i ←i + 1 and go to step 1.If there are unallocated M-2 nodes when all initial overlaps
are modified, new overlaps should be established to allocatethese unallocated M-2 nodes in the following. Generate asequence of power-law RVs with exponent PROsize. The sumof these RVs should be equal to the amount of all the unallo-cated M-2 individuals; otherwise, regenerate all these RVs.For a generated RV RVi, randomly select two communitieswith no overlap. In addition, the remaining available space ofthe two selected communities should be not less than RVi.Establish an overlap between the two communities, and ran-domly allocate M-2 nodes with the number of RVi into thisoverlap and the corresponding two communities.
Finally, each M-1 node is randomly allocated into an unsat-urated community, and the overlapping community structurefor one period in a day is established. For each period of aday, construct an overlapping community structure by usingthe same mechanism, and the overlapping community struc-tures in the same period of different days are the same.
Generate Local Degree — As we have analyzed, a node’s localpopularity depends on its local degree. Let Localni denote thelocal degree of a node n in its community i (according to thedefinition of the k-clique community, Localni ≥ k–1). To reflectlocal heterogeneous popularity, we assume that the value ofLocalni –(k – 1) follows a power-law distribution with exponentPRLocal. For each period of one day, we use an RV generatorto assign the value of Localni for each node n in its community i.
From Social Relationships to Human MobilitySince social network structure is not the sole factor in physicalhuman mobility, many realistic individual physical mobilityfeatures (e.g., Levy motion [15] and location preferences [12])can be adopted in the HHW model based on the establishedoverlapping communities above. However, in order to focuson social network structural aspects of mobility, the HHWmodel only adopts a simple scheme based on location prefer-ential attachment as follows.
Social Relationship and Geographical Proximity Mapping —HHW model simulates N mobile nodes in a two-dimensionsquare plane. The two-dimensional simulation plane is dividedinto a grid composed of non-overlapping square cells. Foreach period during a day, each community with the size ofScom is randomly associated with a zone composed of Scom
connected cells (geographical proximity). Each node n is ran-domly associated with Localni cells within the associated zoneof its community i. For the same period on different days, theassociated zone of each community and the associated cell ofeach node are the same.
Movement of Individuals — At the beginning, each node firstrandomly selects one of all its associated cells, then is locatedat a random position inside the selected cell. To drive move-ment, a goal is assigned to each node. The goal is randomlyselected inside one of all its associated cells and then the nodemoves towards this goal. When a node reaches its currentgoal, it waits for a power-law distribution pause time, andthen selects and moves toward a new goal based on the samemechanism. When a new period starts, the overlapping com-munity structure and the corresponding associated zones andcells change. Having reached its current goal, each nodeselects a new goal according to its new associated cells duringthis new period.
Since a node with a larger local degree in a community hasmore associated cells in this community, it has greater oppor-
Figure 3. An example of the eight initial overlaps constructed by two M-3 nodes.
MN1=4
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tunity to meet other nodes in this community. Similarly, nodeswith larger membership numbers have greater opportunity tomeet other nodes from more communities. Consequently, thissimple mobility scheme can reflect both the local and globalpopularity.
EvaluationWe verify whether the HHW model can synthetically generatethe statistical features observed from real social networks andmobile user traces. As mentioned, in various real social net-works PRMN ≥ 2, PROsize ≥ 2, and 1 < PRCsize < 1.6 [8]. Forreality, the values of these three input parameters were select-ed according to the above constraints in all simulations. Dueto page limits, we only consider a 4-clique overlapping com-munity structure.
Established Overlapping Community StructureValidationFigure 4 shows the statistical distributions of overlap size,community size, and membership number of the syntheticallygenerated 4-clique overlapping community structure with apopulation of 1000 individuals. In this simulation we setPRMN = 3, PROsize = 2, and PRCsize = 1.2. The result demon-strates that the HHW model manages to generate a realisticoverlapping community structure with three given statisticalconstraints.
Inter-Contact Time Distribution and HeterogeneousHuman PopularityWe now verify how well HHW models the statistical featuresof human mobility, considering a scenario composed of 200individuals in a simulation plane of 5 km × 5 km, divided intoa grid composed of 62,500 cells (i.e., the length of each cellwas 20 m). In this set of simulations we set PRMN = 2, PRO-size = 2, and PRLocal = 2.4. The transmission range of thewireless device carried by each individual was set to 20 m. Thespeed of each individual was uniformly distributed between 1and 6 m/s, and the exponent of the power-law pause time wasset to 2. The simulation time was two days, and each day wasdivided into three identical periods (i.e., 8 h).
The evaluation of inter-contact time distribution is a wellaccepted approach to evaluating the reality of a mobilitymodel, because it significantly influences the performance ofdata forwarding schemes [3]. Figure 5 shows that the HHWmodel manages to generate inter-contact time distributionsthat are similar to those of the real traces.
Borrel et al. [5] experimentally show that the underlyingsocial graph structure has negligible impact on inter-contacttime distribution of the SIMPS model. However, this conclu-sion is only for the SIMPS model. Furthermore, the inter-con-tact time distribution is not the unique human mobility patternthat has a significant impact on data forwarding protocols.Consequently, this does not contradict our motivation of usinga realistic social network structure to model human mobility.
We use “betweenness centrality” [17] to quantitatively mea-sure the heterogeneous human popularity, which is also usedin Hui et al. [1]. Unlimited flooding with different uniformlydistributed traffic patterns and randomly selected source-des-tination pairs was generated for real traces and mobility mod-els. A node’s popularity was calculated as the number of timesit fell on all the shortest paths of source-destination pairs.
Figure 6a clearly shows there are a small number of nodesthat have extremely high relaying ability, and a large numberof nodes that have moderate or low betweenness centralityvalues in the INFOCOM 2006 trace. For the human heteroge-neous popularity of other real traces, we refer the reader toHui et al. [1].
We first compare the HHW model with a representativereal-trace-based model, LW [15]. The simulation scenario isthe same as that of the HHW model (200 nodes in a 5 km × 5km area for two days). We set the two input parameters ofthe LW model as α = 1.4 and β = 1.8. Figure 6b clearlyshows that the LW model fails to exhibit heterogeneoushuman popularity, while Fig. 6c shows that the HHW model isable to exhibit similar heterogeneity to that of the INFOCOM2006 trace.
In order to clearly demonstrate the causes of heteroge-neous popularity in detail, we run simulations for threedegraded versions of HHW models. Figure 6d illustrates theHHW model with homogeneous local degree distribution(nodes’ local degree follows exponential distribution). Conse-quently, the heterogeneity of human popularity shown in Fig.
Figure 4. CCDF of synthetically generated overlapping communitystructure.
Figure 6. Number of times a node as relays for others in real and HHW generated traces. (a) INFOCOM 06; (b)LW model; (c) HHWmodel; (d) HHW model with homogeneous local degree; (e)HHW model with non-overlapping community structure; (f) HHW modelwith homogeneous local degree and non-overlapping community structure.
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6d comes from the overlapping community structure (i.e.,nodes’ pure heterogeneous global popularity). Figure 6eshows the HHW model with non-overlapping communitystructure, (i.e., each node is in a separate community). There-fore, the heterogeneity in Fig. 6e is generated by nodes’ pureheterogeneous local popularity (local degree with power-lawdistribution). Finally, Fig. 6f clearly shows that nodes’ popu-larities become nearly homogeneous when there is no overlapbetween communities and nodes’ local degrees are exponen-tially distributed.
The simulations above demonstrate that the foundationalcause of human heterogeneous popularity is individuals’diverse ranks in a society rather than an individual’s specialphysical motion behaviors.
ConclusionSince people’s social interactions strongly affect their move-ment decisions, understanding social network structure plays asignificant role in accurately modeling their mobility. In thisarticle we briefly describe overlapping community structure inreal social networks and review recent representative humanmobility models. Then, we propose the HeterogeneousHuman Walk (HHW) mobility model based on the analysis ofhow overlapping community structure and individuals’ localdegrees contribute to heterogeneous human mobility popular-ity. For low complexity, the HHW model synthetically con-structs the k-clique overlapping community structure usingsimple algorithms. In addition, HHW also captures the spatio-temporal regularity of individuals’ movement behaviors. Simu-lation results demonstrate that HHW manages to exhibitstatistical characteristics observed from both real social net-works and mobile user traces. Due to the diversity of people’sdaily lives, how to model their asynchronously time-varyingsocial roles is worth further study.
AcknowledgmentThe authors would like to thank the anonymous reviewers fortheir constructive comments, which greatly improved the qual-ity of this article.
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BiographiesSHUSEN YANG ([email protected]) is a Ph.D. candidate in the Departmentof Computing at Imperial College London. He received M.S. and B.S. degreesfrom the Department of Computer Science and Technology at Xi’an Jiaotong Uni-versity in 2006 and 2008, respectively. His research interests are mobility mod-eling and networking algorithms including routing, load balancing, and resourceallocation in wireless self-organizing networks.
XINYU YANG ([email protected]) is a professor in the Department of ComputerScience and Technology at Xi’an Jiaotong University, where he received B.S.,M.S., and Ph.D. degrees in 1995, 1997, and 2001, respectively. He wasawarded the title of “New Century Excellent Talent” by the Chinese Ministry ofEducation in 2009. His research interests include social networks and wirelessnetworking.
CHAO ZHANG ([email protected]) received his M.S. degree from the Depart-ment of Computer Science and Technology at Xi’an Jiaotong University. Hisresearch interests are in the area of mobility modeling and delay-tolerant net-working.
EVANGELOS SPYROU ([email protected]) is a Ph.D. candidate in theDepartment of Computing at Imperial College London. He received his B.Sc. andM.Sc. degrees from Northumbria University at Newcastle in 2002 and 2006,respectively. His research interests are mobility modeling and distributed algo-rithms for self-organization and self-healing in wireless networks.
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