Efficient community detection in large networks using content and links

Abstract

In this paper we discuss a very simple approach of combining content and link information in graph structures for the purpose of community discovery, a fundamental task in network analysis. Our approach hinges on the basic intuition that many networks contain noise in the link structure and that content information can help strengthen the community signal. This enables ones to eliminate the impact of noise (false positives and false negatives), which is particularly prevalent in online social networks and Web-scale information networks.Specifically we introduce a measure of signal strength between two nodes in the network by fusing their link strength with content similarity. Link strength is estimated based on whether the link is likely (with high probability) to reside within a community. Content similarity is estimated through cosine similarity or Jaccard coefficient. We discuss a simple mechanism for fusing content and link similarity. We then present a biased edge sampling procedure which retains edges that are locally relevant for each graph node. The resulting backbone graph can be clustered using standard community discovery algorithms such as Metis and Markov clustering.Through extensive experiments on multiple real-world datasets (Flickr, Wikipedia and CiteSeer) with varying sizes and characteristics, we demonstrate the effectiveness and efficiency of our methods over state-of-the-art learning and mining approaches several of which also attempt to combine link and content analysis for the purposes of community discovery. Specifically we always find a qualitative benefit when combining content with link analysis. Additionally our biased graph sampling approach realizes a quantitative benefit in that it is typically several orders of magnitude faster than competing approaches.

A Survey on Information Diffusion in Online Social Networks: Models and Methods

“…The main challenge is how to detect those communities that have high influence within a social network, and many methods have been proposed to this end, mainly including links and attributes. Previous research has been able to detect communities by way of social links, content [44][45][46], node attributes [47], sentiment topics [48], and others [49].…”

“…Ruan et al [46] also support the viewpoint that links combine with contents, but suggest that the method is not as efficient as it could be. They proposed an efficient CODICIL method for detecting communities by way of combining links and contents.…”

An Improved Random Walk Based Community Detection Algorithm

“…In addition, there's selection method classifies networks into four categories and selects different detection algorithms based on the characteristics of the given network [15]. Ruan's algorithms delete (add) edges from (to) the network according to the semaphore obtained by mixing attribute similarity and link similarity and use existing community detection algorithms to find community structure [16]. By preprocessing the network, these algorithms can achieve more realistic results than previous methods, but this will lead to a negative impact on the performance of the algorithm.…”

A method for characterizing communities in dynamic attributed complex networks

“…[3]for a detailed review). Most of the existing methods deal with plain complex networks, but new methods were gradually introduced to handle richer networks: first link directions and weights, then time, and more recently node attributes [4][5][6][7][8].…”