Group-centrality is an emerging area in Network Science which seeks to quantify the importance of nodes within and between communities. Previous group-centrality measures either have strong theoretical underpinnings in community detection, or scale well, but not both.We propose an extremely scalable measure, modularity-impact, that is directly derived from Newman's modularity. Unlike other measures, modularity-impact is signed, providing additional information about each node's role : hub within its community , or bridge between communities. We analyze our results in a network robustness framework, as it is a generalized version of immunization under SIR models, which prior works have used for evaluation. We show that our measure out-performs previous measures for immunizing strategies in generated networks with strong community structure. Additionally, we show that our measure gives the most effective attack strategy for a million-node infrastructure network, fragmenting it while removing less than 2\% of nodes. This is over 8 times more fragile than can be seen with previous group-centralities. We further find that the social media communication network demonstrates extreme robustness to group-centrality attacks, suggesting that user-based interventions to mitigate misinformation diffusion will be ineffective. Finally, we show that our group-centrality measure can be used as a greedy solution to the community deception problem .
Community-aware centrality is an emerging research area in network science concerned with the importance of nodes in relation to community structure. Measures are a function of a network's structure and a given partition. Previous approaches extend classical centrality measures to account for community structure with little connection to community detection theory. In contrast, we propose cluster-quality vitality measures, i.e., modularity vitality, a community-aware measure which is well-grounded in both centrality and community detection theory. Modularity vitality quantifies positive and negative contributions to community structure, which indicate a node's role as a community bridge or hub. We derive a computationally efficient method of calculating modularity vitality for all nodes in O(M + NC) time, where C is the number of communities. We systematically fragment networks by removing central nodes, and find that modularity vitality consistently outperforms existing community-aware centrality measures. Modularity vitality is over 8 times more effective than the next-best method on a million-node infrastructure network. This result does not generalize to social media communication networks, which exhibit extreme robustness to all community-aware centrality attacks. This robustness suggests that user-based interventions to mitigate misinformation diffusion will be ineffective. Finally, we demonstrate that modularity vitality provides a new approach to community-deception .
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Group-centrality
Community-aware centrality
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area in Network Science which seeks to quantify
research area in network science concerned with
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within and between communities. Previous group-centrality measures either have strong theoretical underpinnings in community detection, or scale well, but not both.We propose an extremely scalable measure, modularity-impact, that is directly derived from Newman's modularity. Unlike other measures, modularity-impact is signed, providing additional information about each
in relation to community structure. Measures are a function of a network's structure and a given partition. Previous approaches extend classical centrality measures to account for community structure with little connection to community detection theory. In contrast, we propose cluster-quality vitality measures, i.e., modularity vitality, a community-aware measure which is well-grounded in both centrality and community detection theory. Modularity vitality quantifies positive and negative contributions to community structure, which indicate a
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: hub within its community , or bridge between communities. We analyze our results in a network robustness framework, as it is a generalized version of immunization under SIR models, which prior works have used for evaluation. We show that our measure out-performs previous measures for immunizing strategies in generated networks with strong community structure. Additionally, we show that our measure gives the most effective attack strategy for a million-node infrastructure network, fragmenting it while removing less than 2\% of nodes. This
as a community bridge or hub. We derive a computationally efficient method of calculating modularity vitality for all nodes in O(M + NC) time, where C is the number of communities. We systematically fragment networks by removing central nodes, and find that modularity vitality consistently outperforms existing community-aware centrality measures. Modularity vitality
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fragile than can be seen with previous group-centralities. We further find that the
effective than the next-best method on a million-node infrastructure network. This result does not generalize to
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network demonstrates
networks, which exhibit
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group-centrality attacks, suggesting
all community-aware centrality attacks. This robustness suggests
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show that our group-centrality measure can be used as a greedy solution to the community deception problem
demonstrate that modularity vitality provides a new approach to community-deception