PICS: Parameter-free Identification of Cohesive Subgroups in Large Attributed Graphs

Akoglu, Leman; Tong, Hanghang; Meeder, Brendan; Faloutsos, Christos

doi:10.1137/1.9781611972825.38

Cited by 101 publications

(92 citation statements)

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“…Therefore, in order to detect metadata groups, non-topological inputs might be necessary. In the most recent literature on community detection several such approaches have been proposed, mostly by computer scientists [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. We stress, however, that structural communities are very important for the function of a network, as they can significantly affect the dynamics of processes taking place on the network, such as diffusion, synchronization, opinion formation, etc.…”

Section: Discussionmentioning

confidence: 99%

Community detection in networks: Structural communities versus ground truth

2014

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Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.

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Section: Discussionmentioning

confidence: 99%

Community detection in networks: Structural communities versus ground truth

2014

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“…For instance, various existed community detection methods have considered network structures and node attributes in a combined manner to detect communities of individual in online social networks (Yang, McAuley, & Leskovec, 2013, December) (Akoglu, Tong, Meeder, & Faloutsos, 2012, April) (Moser, Ge, & Ester, 2007, August). Unfortunately, these methods cannot detect overlapping communities.…”

Section: Related Workmentioning

confidence: 99%

Community Detection Using Node Attributes and Structural Patterns in Online Social Networks

Singh¹,

Rahman²,

Miah³

et al. 2017

CIS

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Community detection in online social networks is a difficult but important phenomenon in term of revealing hidden relationships patterns among people so that we can understand human behaviors in term of social-economics perspectives. Community detection algorithms allow us to discover these types of patterns in online social networks. Identifying and detecting communities are not only of particular importance but also have immediate applications. For this reason, researchers have been intensively investigated to implement efficient algorithms to detect community in recent years. In this paper, we introduce set theory to address the community detection problem considering node attributes and network structural patterns. We also formulate probability theory to detect the overlapping community in online social network. Furthermore, we extend our focus on the comparative analysis on some existing community detection methods, which basically consider node attributes and edge contents for detecting community. We conduct comprehensive analysis on our framework so that we justify the performance of our proposed model. The experimental results show the effectiveness of the proposed approach.

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“…The representative methods [2,14,17,20,27,34] aim to partition the given graph into structurally dense and attributewise homogeneous clusters, detect deviations from frequent subgraphs [25], or search for community outliers in attributed graphs [14]. These methods, however, enforce attribute homogeneity in all attributes.…”

Section: Graph Mining On Attributed Graphsmentioning

confidence: 99%

“…Recent methods have been proposed for attributed graphs, however, they either use all the given attributes [2,14,20,34] or they perform an unsupervised feature selection [16,18,22,26]. In contrast to all of these graph mining paradigms (cf.…”

Section: Introductionmentioning

confidence: 99%

“…The graph represents the friendship relations and the node attributes denote degree, location, mother tongue, and work. There are two focused clusters: On the [19], Spectral [24], Co-clustering [10] Autopart, Cross-associations [7] PageRank-Nibble [3], [30], BigClam [33] Spectral Relational [20], SA-Cluster [34] CoPaM [22], Gamer [16] PICS [2] CODA [14] GOutRank [23], ConSub [18] FocusCO [this paper] left, people know each other due to their degrees and locations. On the right, a similarity in work induces the second cluster.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Focused clustering and outlier detection in large attributed graphs

Perozzi

Akoglu

Sánchez

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Self Cite

202

106

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Graph clustering and graph outlier detection have been studied extensively on plain graphs, with various applications. Recently, algorithms have been extended to graphs with attributes as often observed in the real-world. However, all of these techniques fail to incorporate the user preference into graph mining, and thus, lack the ability to steer algorithms to more interesting parts of the attributed graph.In this work, we overcome this limitation and introduce a novel user-oriented approach for mining attributed graphs. The key aspect of our approach is to infer user preference by the so-called focus attributes through a set of user-provided exemplar nodes. In this new problem setting, clusters and outliers are then simultaneously mined according to this user preference. Specifically, our FocusCO algorithm identifies the focus, extracts focused clusters and detects outliers. Moreover, FocusCO scales well with graph size, since we perform a local clustering of interest to the user rather than global partitioning of the entire graph. We show the effectiveness and scalability of our method on synthetic and realworld graphs, as compared to both existing graph clustering and outlier detection approaches.

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PICS: Parameter-free Identification of Cohesive Subgroups in Large Attributed Graphs

Cited by 101 publications

References 0 publications

Community detection in networks: Structural communities versus ground truth

Community detection in networks: Structural communities versus ground truth

Community Detection Using Node Attributes and Structural Patterns in Online Social Networks

Focused clustering and outlier detection in large attributed graphs

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