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.

• The paper presents CODICIL, a novel framework that integrates content and link data to mitigate noise in large-scale networks.
• It utilizes cosine similarity, Jaccard coefficient, and probabilistic link strength to enhance community detection accuracy.
• Empirical evaluations on Flickr, Wikipedia, and CiteSeer demonstrate significant improvements in speed and quality over traditional methods.

Efficient Community Detection in Large Networks using Content and Links

In the study "Efficient Community Detection in Large Networks using Content and Links," Ruan, Fuhry, and Parthasarathy propose a method for community discovery by integrating content and link-based information. This paper addresses the challenges posed by noisy and massive-scale networks, emphasizing the potential of leveraging node content to enhance community detection, which can often be skewed by erroneous structural links within online social networks and other large-scale information systems.

The authors introduce a novel measure for estimating the strength of a connection between two nodes by combining link strength with content similarity. Here, link strength is derived probabilistically, reflecting the likelihood of a link existing within a specific community. For content similarity, they employ metrics like cosine similarity and Jaccard coefficient. They subsequently devise a framework, CODICIL (COmmunity Discovery Inferred from Content Information and Link-structure), for fusing these data elements in a way that efficiently reduces the network size by biased edge sampling. This results in a simplified backbone graph that preserves essential community structures, ready to be clustered using algorithms such as Metis and Markov clustering.

Using empirical data sets from Flickr, Wikipedia, and CiteSeer, the authors conduct extensive experiments to validate their methodology. They demonstrate consistent qualitative improvements from integrating content, achieving speed and performance that surpasses traditional methods that either rely solely on links or incorporate content at the cost of computational scalability. Their results show CODICIL runs orders of magnitude faster while maintaining or improving the quality of detected communities.

The practical implications of this work are substantial, addressing the increasing prevalence of massive online networks where efficient and accurate community detection is critical. By reducing the computational complexity typically involved with large-scale networks, this approach holds promise for applications in social media analytics, web search optimizations, and numerous areas where rapid, reliable network analysis is indispensable.

From a theoretical perspective, this work underscores the significance of hybrid approaches in network analysis. Combining topological and content-based methods effectively mitigates the weaknesses that arise when considering these elements in isolation.

Looking toward the future, further investigations could explore the dynamic aspects of network structures and content evolution over time, potentially enhancing real-time applications of community detection. Additionally, extending the current model to accommodate weighted links or multi-attribute node information could further enrich its applicability in increasingly complex network scenarios.

In conclusion, this paper offers a compelling argument for integrating content with structural data for community detection in large networks, advancing both the theoretical understanding and practical methodologies available in the field of network analysis.