Abstract
Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.
Highlights
Many social networks in practice commonly exhibit the property of containing community structure [1,2], i.e., they naturally divide into groups of nodes with denser connections inside each group and fewer connections crossing between groups
As depicted in their subfigures, the Normalized Mutual Information (NMI) values and modularities indicated by our Quick Community Adaptation (QCA) method, in general, are very high and competitive with those of OSLOM while are much better than those produced by MIEN and FacetNet methods
OSLOM tends to perform better than QCA in the first couple of network snapshots, its performance is taken over by QCA when the networks evolve over time, especially at the end of the evolution where OSLM reveals big gaps in similarity to the planted network communities (Note that the higher NMI score at the end of the evolution, the better the final detected community structure)
Summary
Many social networks in practice commonly exhibit the property of containing community structure [1,2], i.e., they naturally divide into groups of nodes with denser connections inside each group and fewer connections crossing between groups. It is noteworthy to differentiate between them While these two problems share the same objective of partitioning network nodes into groups, the number of clusters in graph clustering is often predefined (or given as a part of the input) whereas the number of communities is typically unknown in community detection. Communities display the whole network organization as a compact and more understandable level where each community can represent a functional group or an entity in the system At this level, community structure provides us meaningful insights into network’s organizational principles, and sheds light on preventing potential vulnerability and security threats such as network corruption and computer virus and worm propagation [3]. Studies on community detection on static networks can be found in an excellent survey [4], as well as in the work of [5,6,7] and references therein
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