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Communication Dans Un Congrès Année : 2020

Finding Influential Nodes in Networks with Community Structure

Zakariya Ghalmane
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Mohammed El Hassouni

Résumé

Identifying influential nodes is a fundamental issue in complex networks. Several centrality measures take advantage of various network topological properties to target the top spreaders. However, the vast majority of works ignore its community structure while it is one of the main properties of many real-world networks. In our previous work 4 , we show that the centrality of a node in a network with non-overlapping communities depends on two features: Its local influence on the nodes belonging to its community, and its global influence on nodes belonging to the other communities. For this end, we introduced a framework to adapt all the classical centrality measures proposed for networks with no community structure to non-overlapping modular networks. We proposed a two-dimensional vector (the so-called "Modular centrality"), where each dimension accounts for a different type of influence that the nodes can exert in the network. Its first component is measured by computing the classical centrality on the Local network. This network is formed only from the intra-community links of the original network. Additionally, its second component is quantified by computing the classical centrality on the Global network. This network is formed only from the inter-community links of the original network. Depending of the strength of the community structure, these two components are more or less influential. In a recent study 5 , we extended this framework to networks with overlapping modules. Indeed, it is a frequent scenario in real-world networks where nodes usually belong to several communities, especially for social networks. The "Overlapping Modular Centrality" is a two-dimensional measure that quantifies the local and global influence of overlapping and non-overlapping nodes. The global component of this vector is defined in the same way as the Modular centrality. It is computed on the global network obtained by removing all the intra-community links from the original network. However, the local component computation depends on the nature of nodes. For a non-overlapping node, as previously, only members of its community are considered. For an overlapping node, all the communities that the node belongs to are merged in a single community. Extensive experiments have been performed on synthetic and real-world data using the Susceptible-Infected-Recovered
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Dates et versions

hal-03059116 , version 1 (13-12-2020)

Identifiants

  • HAL Id : hal-03059116 , version 1

Citer

Zakariya Ghalmane, Stephany Rajeh, Chantal Cherifi, Hocine Cherifi, Mohammed El Hassouni. Finding Influential Nodes in Networks with Community Structure. Network Modeling, Learning and Analysis (NMLA), WorldCIST2020 Workshop, Apr 2020, Budva (Online), Montenegro. pp.3. ⟨hal-03059116⟩
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