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Relative Validity Criteria for Community Mining Evaluation
ASONAM 2012
Reihaneh Rabbany, Mansoreh Takaffoli, Justin Fagnan, Osmar R. Zaϊane and Ricardo J. G. B. CampelloDepartment of Computing Science,University of Alberta,Edmonton, Canada
Aug 2012
Motivation
Applications in different domains; sociology, criminology •Module identification in Biological Networks Clusters in Protein-Protein Interaction Networks Protein complexes and parts of pathways; Clusters in a
protein similarity network protein families. (R Guimerà et al., Functional cartography of complex metabolic networks, Nature 433, 2005)
Prerequisite of further analysis; Targeted advertising, link prediction, recommendation
•Social Networks: personalized news feed, easier privacy settingsGmail's "Don't Forget Bob!" and "Got the Wrong Bob?" features (M Roth et al., Suggesting Friends Using
the Implicit Social Graph, KDD 2010)
•Citation network of scholarsPaper and collaborator recommendation, Network visualization and Navigation; e.g. CiteULike, Arnet Miner
and Microsoft Academic
•Hyperlinks between web pages - WWW Detecting Group of closely related topics to refined search results (J Chen et al., An Unsupervised Approach
to Cluster Web Search Results Based on Word Sense Communities. Web Intelligence 2008)
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Loosely defined as groups of nodes that have relatively more links between themselves than to the rest of the network
• Nodes that have structural similarity (SCAN, Xu et al. 2007)
• Nodes that are connected with cliques (CFinder by Palla et al. 2005)
• Nodes that a random walk is likely to trap within them (MCL by Dongen, Walktrap by Pons and Latapy)
• Nodes that follow the same leader (TopLeaders, 2010)
• Nodes that make the graph compress efficiently (Infomap, Infomod, Rosvall and Bergstrom, 2011)
• Nodes that are separated from the rest by min cut, conductance (flow based methods, e.g. Kernighan-Lin (KL), betweenness of Newman)
• Nodes that number of links between them is more than chance (Newman's Q modularity, FastModularity, Blondel et al.’s Louvain)
Community
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Evaluation; overlooked
Internal EvaluationPredefined quality/structure for the communities
• Graph partitioning measures (density, conductance)
External EvaluationAgreement between the results and a given known ground-truth•A clustering similarity/agreement indexes; Rand Index, Jaccard •Benchmarks with ground truth; GN(2002), LFR(2008)
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The community structure is not known beforehand The community structure is not known beforehand
No ground truthNo large data set with known ground truth
The synthetic benchmarks disagree with some real network characteristics
No ground truthNo large data set with known ground truth
The synthetic benchmarks disagree with some real network characteristics
LFRGNKarate
Relative Validity Criteria
Validity criteria defined for clustering evaluation; compares different clusterings of a same data setWe altered criteria •Generalized distance; graph distance measures•Generalized mean/centroid notion; averaging v.s. medoid e.g. Variance Ratio Criterion (VRC)
Same for: Dunn index, Silhouette Width Criterion (SWC), Alternative Silhouette, PBM, C-Index, Z-Statistics, Point-Biserial (PB)
Distance Alternatives: Edge Path (ED), Shortest Path Distance (SPD), Adjacency Relation Distance (ARD), Neighbour Overlap Distance (NOD), Pearson Correlation Distance (PCD), ICloseness Distance (ICD)
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Correlation with External Index
Correlation of relative criteria and external scores on different clusterings of same data set
random clusterings that range from very close to very far from ground truth
For karate;
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Correlation with External Index
Correlation of relative criteria and external scores on different clusterings of same data set
random clusterings that range from very close to very far from ground truth
For karate;
5
Ranking of Criteria on Real World Benchmarks
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Data set statistics
Overall Ranking
Difficulty Analysis
Ranking of Criteria on Synthetic Benchmarks
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Data set statistics
Overall ranking for very mixed communities
Ranking for well separated communities
Criteria Ranking is affected by:• Choice of benchmarks, synthetic generator and its parameters• Choice of External agreement Index; ARI, NMI, AMI, Jacard• Choice of correlation measure; Pearson & Spearman correlation• Choice of clustering randomization
Get the ranking in your setting
www.cs.ualberta.ca/~rabbanyk/criteriaComparison
Ranking varies
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Evaluation Issues• Community mining specific agreement
measure• Realistic synthetic benchmarks Extensions of criteria• Incorporating attributes; combine
clustering and community mining for cases for which we have both attributes and relations
• Incorporating uncertainty and edges with probability
• ...
Future Works
9
End
Questions?
10
Alternative Distances
A
• Edge Path (ED),
• Shortest Path Distance (SPD),
• Adjacency Relation Distance (ARD),
• Neighbour Overlap Distance (NOD),
• Pearson Correlation Distance (PCD),
• ICloseness Distance (ICD)
Relative criteria
• Variance Ratio Criterion (VRC)
• Dunn index,
• Silhouette Width Criterion (SWC),
• Alternative Silhouette,
• PBM,
• Davies-Bouldin
• C-Index,
• Point-Biserial (PB)
B