Abstract
Identifying influential nodes in social networks − by determining the most effective and efficient set of primary users − is a
crucial task for maximizing the spread of influence. The competitive world of social networks and the variety of services
they provide have persuaded users to apply for the membership of several networks. This situation makes involvement of
the shared individuals across multiple social networks essential for the effective dissemination of information. In this paper,
we introduce a community-based entropic method to identify influential nodes in multiple social networks, taking into
account the effect of shared users. The proposed method, titled IM2-SE, focuses on calculating a total structural entropy for
each node. This structural entropy is defined as the weighted sum of the structural entropies of the node in the networks and
in the communities, it belongs to. The structural entropy of a node is based on Tsallis entropy considering both local and
global structures of the network, and it utilizes the degree of centrality and betweenness centrality. Then, the nodes with the
highest total structural entropy are identified as the influential nodes. The empirical evaluation of the proposed method on
the real-world datasets including homogeneous and heterogeneous networks, demonstrates its superior performance to the
baseline methods. Furthermore, the impact of the shared nodes between networks in identifying the influential nodes has
been examined, revealing that up to 50% of the identified influential nodes are the shared nodes between specific networks.
crucial task for maximizing the spread of influence. The competitive world of social networks and the variety of services
they provide have persuaded users to apply for the membership of several networks. This situation makes involvement of
the shared individuals across multiple social networks essential for the effective dissemination of information. In this paper,
we introduce a community-based entropic method to identify influential nodes in multiple social networks, taking into
account the effect of shared users. The proposed method, titled IM2-SE, focuses on calculating a total structural entropy for
each node. This structural entropy is defined as the weighted sum of the structural entropies of the node in the networks and
in the communities, it belongs to. The structural entropy of a node is based on Tsallis entropy considering both local and
global structures of the network, and it utilizes the degree of centrality and betweenness centrality. Then, the nodes with the
highest total structural entropy are identified as the influential nodes. The empirical evaluation of the proposed method on
the real-world datasets including homogeneous and heterogeneous networks, demonstrates its superior performance to the
baseline methods. Furthermore, the impact of the shared nodes between networks in identifying the influential nodes has
been examined, revealing that up to 50% of the identified influential nodes are the shared nodes between specific networks.
| Original language | English |
|---|---|
| Number of pages | 22 |
| Journal | Social Network Analysis and Mining |
| Volume | 15 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 20 Mar 2025 |
Bibliographical note
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source,
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Keywords
- Influence Maximization
- Influential Nodes
- Multiple Social Networks
- Community Detection
- Structural Entropy
- Tsallis Entropy