Identifying Influential Nodes Based on Community Structure to Speed up the Dissemination of Information in Complex Network

Tulu, Muluneh Mekonnen; Hou, Ronghui; Younas, Talha

doi:10.1109/access.2018.2794324

Cited by 97 publications

(63 citation statements)

References 29 publications

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“…Structurally, this implies compartmentalized conversations that could provide evidence for homophily, filter bubbles, or echo chambers in which members with similar traits are highly connected [65,66,67]. Furthermore, such structures are indicative of information dissemination [68], a limited form of science communication that emphasizes distributing information over participatory conversation. Similar community cluster networks have been shown to form around topics such as #globalhealth [39], a broad general topic with a wide variety of subtopics, areas of foci, and issues such as infectious diseases, pollution, chronic non-communicable diseases, and climate change.…”

Section: Discussionmentioning

confidence: 99%

Scientific Twitter: The flow of paleontological communication across a topic network

2019

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The field of paleontology, which is based principally on observations of the natural world, includes an active community that is engaged across multiple social media platforms, consisting of museums, academic researchers, amateur fossil collectors, paleontological artists, and commercial fossil dealers. As such, it represents an ideal environment for examining the people, interactions, and flow of scientific information. Using interactions involving the four most popular Twitter hashtags for paleontology, this embedded mixed methods study defined the members of this social world and investigated how they influenced and controlled the flow of information, as well as how their expression of scientific practice was related to their identity. Results provide further evidence for the diversity of people and practice involved in this domain of science and indicate that the magnitude and breadth of the public’s impact may be larger than previously projected. Certain types of messages were shown to be effective for different segments of the community, but news posts, essentially media outlet stories, were ineffective for generating any form of engagement. This study adds to our understanding of the important scientific contribution being made by members of the public as they interact with professional scientists and educators as peers in an open social media platform that supports a diverse and active community.

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Section: Discussionmentioning

confidence: 99%

Scientific Twitter: The flow of paleontological communication across a topic network

2019

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“…In real-world networks, nodes are usually found to be naturally arranged into various groups or communities. Numerous investigations have been conducted to model and to analyze the community structure of a network 11,[43][44][45][46][47][48][49] . Yet, there is no unique interpretation of the community structure.…”

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confidence: 99%

Extracting backbones in weighted modular complex networks

Ghalmane¹,

Cherifi²,

Cherifi³

2020

Sci Rep

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Network science provides effective tools to model and analyze complex systems. However, the increasing size of real-world networks becomes a major hurdle in order to understand their structure and topological features. Therefore, mapping the original network into a smaller one while preserving its information is an important issue. Extracting the so-called backbone of a network is a very challenging problem that is generally handled either by coarse-graining or filter-based methods. Coarse-graining methods reduce the network size by grouping similar nodes, while filter-based methods prune the network by discarding nodes or edges based on a statistical property. In this paper, we propose and investigate two filter-based methods exploiting the overlapping community structure in order to extract the backbone in weighted networks. Indeed, highly connected nodes (hubs) and overlapping nodes are at the heart of the network. In the first method, called “overlapping nodes ego backbone”, the backbone is formed simply from the set of overlapping nodes and their neighbors. In the second method, called “overlapping nodes and hubs backbone”, the backbone is formed from the set of overlapping nodes and the hubs. For both methods, the links with the lowest weights are removed from the network as long as a backbone with a single connected component is preserved. Experiments have been performed on real-world weighted networks originating from various domains (social, co-appearance, collaboration, biological, and technological) and different sizes. Results show that both backbone extraction methods are quite similar. Furthermore, comparison with the most influential alternative filtering method demonstrates the greater ability of the proposed backbones extraction methods to uncover the most relevant parts of the network.

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“…After dividing the network into communities, they find the node with the greatest local influence in each community, and then find the node with the greatest influence in the whole network. M. M. Tulu [ 19 ] et al calculated the node’s Shannon Entropy as the node’s importance by using the number of nodes outside the community and the number of nodes inside the community after the community was divided. Zhao [ 20 ] measured the importance of nodes by the number of communities which the nodes connected to after dividing the network into communities.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Algorithm of the Importance of Network Node Based on Community Influence

He¹,

Luo²,

Yin³

2020

Data Mining and Big Data

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Identifying nodes in social networks that have great influence on information dissemination is of great significance for monitoring and guiding information dissemination. There are few methods to study the influence of communities on social networks among the existing node importance evaluation algorithms, and it is difficult to find nodes that promote information dissemination among communities. In view of this reason, this paper proposes a node importance evaluation algorithm based on community influence (abbreviated as IEBoCI algorithm), which evaluates the importance of the nodes based on the influence degree of the nodes on the communities and the ability to disseminate information the communities to which the nodes are connected. This algorithm firstly calculates the activation probability of nodes to other nodes, which is used to divide communities and evaluate influence. Secondly, the network is divided into communities based on LPA algorithm. Finally, the importance of the node is calculated by combining the influence of the community itself and the influence of the node on the community. Experiments are carried out on real social network data and compared with other community-based methods to verify the effectiveness of the algorithm.

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Identifying Influential Nodes Based on Community Structure to Speed up the Dissemination of Information in Complex Network

Cited by 97 publications

References 29 publications

Scientific Twitter: The flow of paleontological communication across a topic network

Scientific Twitter: The flow of paleontological communication across a topic network

Extracting backbones in weighted modular complex networks

An Evaluation Algorithm of the Importance of Network Node Based on Community Influence

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