Detecting Communities in Social Networks using Max-Min Modularity

Abstract

Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common property of these networks is their community structure, considered as clusters of densely connected groups of vertices, with only sparser connections between groups. The identification of such communities relies on some notion of clustering or density measure, which defines the communities that can be found. However, previous community detection methods usually apply the same structural measure on all kinds of networks, despite their distinct dissimilar features. In this paper, we present a new community mining measure, Max-Min Modularity, which considers both connected pairs and criteria defined by domain experts in finding communities, and then specify a hierarchical clustering algorithm to detect communities in networks. When applied to real world networks for which the community structures are already known, our method shows improvement over previous algorithms. In addition, when applied to randomly generated networks for which we only have approximate information about communities, it gives promising results which shows the algorithm's robustness against noise.

Survey on Community Detection in Online Social Networks

“…al [12] proposed agglomerative clustering algorithm along with Max-min modularity quality measure. Proposed algorithm considers both topology of network and provided domain knowledge e.g.…”

Detection of Communities and Bridges in Weighted Networks

“…Techniques for identifying groups or communities within a network can be classified into two different categories: (i) graph partitioning based approaches [5,15,16], and (ii) modularity scoring based approaches [1,3,4,6,22,30,34]. Graph partitioning based methods generally partition different nodes into groups that share common features or topologies.…”

Patterns on the Connected Components of Terabyte-Scale Graphs

“…Graph Mining: There exists lots of "graph mining" algorithms: subgraph discovery(e.g., [12], [13], gPrune [14], gApprox [15], gSpan [16], Subdue [17], ADI [18], CSV [19]), computing communities (eg., [20], DENGRAPH [21], METIS [22]), attack detection [23], with too many alternatives for each of the above tasks. They are not directly related to the focus of this paper which is the static and dynamic structures of real networks.…”