Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion (1503.07439v2)

Abstract: Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this paper, we propose an efficient overlapping community detection algorithm using a seed expansion approach. The key idea of our algorithm is to find good seeds, and then greedily expand these seeds based on a community metric. Within this seed expansion method, we investigate the problem of how to determine good seed nodes in a graph. In particular, we develop new seeding strategies for a personalized PageRank clustering scheme that optimizes the conductance community score. Experimental results show that our seed expansion algorithm outperforms other state-of-the-art overlapping community detection methods in terms of producing cohesive clusters and identifying ground-truth communities. We also show that our new seeding strategies are better than existing strategies, and are thus effective in finding good overlapping communities in real-world networks.

• The paper introduces NISE, a novel algorithm that uses neighborhood-inflated seed expansion to detect overlapping communities in complex networks.
• It employs innovative seeding strategies—Graclus centers and Spread hubs—to select optimal seed nodes for effective community growth.
• Experimental results show that NISE outperforms state-of-the-art methods by producing coherent clusters with lower conductance on large-scale datasets.

Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion

The paper "Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion" presents a novel algorithm designed to identify overlapping communities within networks, a problem of significant interest in the field of network analysis. The authors propose a method named NISE (Neighborhood-Inflated Seed Expansion), which utilizes a seed expansion approach enhanced by personalized PageRank vectors to effectively identify communities within large-scale real-world networks.

Methodological Overview

The authors explore a local expansion technique for community detection that emphasizes the choice of seed nodes followed by a neighborhood-inflated expansion using personalized PageRank (PPR). Specifically, NISE identifies good seeds through two innovative seeding strategies: "Graclus centers" and "Spread hubs." The former relies on employing a multi-level weighted kernel k-means strategy, leveraging the Graclus clustering method to locate central vertices within clusters. The latter, "Spread hubs," selects an independent set of high-degree vertices, inspired by the association of significant communities with high-degree vertices prevalent in power-law distributed networks.

The expansion phase uses the entire vertex neighborhood as the restart region within the PPR clustering process, which the authors identify as critical to the method's success. This inclusion of the neighborhood results in more robust clusters that encapsulate the community's dense core and its boundary-spanning nodes.

Results and Discussion

The paper rigorously tests the NISE algorithm against existing state-of-the-art community detection methods, namely Bigclam, Demon, and Oslom, across various real-world networks, including social networks and citation networks. Key findings reveal that NISE outperforms these methods in producing cohesive clusters with lower conductance, demonstrating high efficacy in precise community detection. Moreover, the computational efficiency of NISE is evident, especially in handling large datasets where other methods typically fail to perform.

An intriguing aspect of NISE's performance is its capacity to find larger and more varied community structures, which previous methods tend to miss. This result underscores the effectiveness of incorporating the entire neighborhood in the PPR expansion stage, allowing NISE to capture complex overlapping structures present in real-world data.

Implications and Future Work

The contributions of this paper lie in redefining the overlap detection approach, focusing on optimized seeding strategies and a robust expansion mechanism via personalized PageRank. The strong numerical results reported highlight the efficiency and scalability of the proposed method, advocating its applicability to a broad range of network structures. These results suggest that this approach holds promise for applications requiring large-scale community detection, such as social media analysis, biological network exploration, and market segmentation.

The paper opens several avenues for future research: exploring more sophisticated seeding strategies that can incorporate domain-specific insights, applying the NISE framework to dynamic or temporal networks where community structures evolve over time, and integrating additional network properties such as edge weights or directionality to refine the method further.

In conclusion, NISE represents a significant advancement in overlapping community detection, offering both theoretical robustness and practical applicability across diverse network contexts. As the field of network analysis grows, methods like NISE will be crucial in unravelling the intricate fabric of interconnections inherent in complex systems.