An Edge Contribution-Based Approach to Identify Influential Nodes from Online Social Networks

Muhuri, Samya; Chakraborty, Susanta Kumar; Setua, S. K.

doi:10.1109/inis.2016.044

Cited by 7 publications

(2 citation statements)

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“…Muhuri and Chakraborty [96] introduced a method that uses Edge Contribution Factor (ECF) for identifying the influential node in an online dynamic network. The ECF of a node v is computed as the ratio of the sum of the neighboring nodes' edge contribution and v's edge contribution to the graph's number of edges.…”

Section: () K Onmentioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top-k influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type analysis scope analysis approach analysis category analysis sub-category analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top-k and influential spreader nodes in social networks.

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Section: () K Onmentioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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“…Previous studies [11]- [14] mostly focused on selecting influential nodes in social network data independently, while they still need identifying other classes of nodes for pivot selection. Reference [15] simply utilized the node centrality to generate the structure-based network and did not consider contexts.…”

Section: Introductionmentioning

confidence: 99%

Building Character Graphs and Dividing Communities in Chinese Novels Based on Graph Data Extraction: Community Division for Character Emotional Polarity Networks

et al. 2020

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Chinese authors indirectly portray real social relationships in society by incorporating their own experiences and feelings about politics, economic life, and cultural habits. Social and natural knowledge are mixed and hidden in natural literary language. Therefore, by well defining the type of original text, the pipeline for processing the text and the rules to build the graph, we can extract enough valid and useful information. Based on higher-level information, we can build a literary character graph to promote computers' comprehension of literature. The identification of opposite characters is a challenging topic because the character relationships are intricate and complex. The cryptic expression in literary works can enhance the readability of the plot, but it is obvious that it increases the cost of understanding. Even for human readers different types of characters are difficult to classify and identify, and it usually needs careful reading for several times to get the subtle differences among the characters. Though extracting knowledge from Chinese literature is a complicated and difficult topic, this paper models the graph of polar literary characters and divides polar communities by extracting polar vertices and polar edges based on the concept of literary sentiment polarity. The results show that using a long-sentence window is a good trade-off. The experiments of modern Chinese polar literature show that the accuracy of the community division method for the integrated graph polarity is obviously better than the method based on the co-occurrence network, and it can automatically match the positive and negative communities as well as build the complete graph structure. It clearly reflects the meaning of literary works from the perspective of polarity. This systematic model describes general standard operation steps for the machine to understand complex Chinese literature. The experiments conducted on seven benchmark Chinese novel datasets demonstrate that the method based on emotional polarity shows a significant improvement compared to baseline performance. Though we use Chinese datasets in this paper, the model and methods are important references for literature analysis and graph extraction in other languages. INDEX TERMS Community division, character graphs, emotional polarity, natural language processing, social network analysis.

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An Estimation Framework of Node Contribution Based on Diffusion Information

et al. 2018

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An Edge Contribution-Based Approach to Identify Influential Nodes from Online Social Networks

Cited by 7 publications

References 10 publications

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Building Character Graphs and Dividing Communities in Chinese Novels Based on Graph Data Extraction: Community Division for Character Emotional Polarity Networks

An Estimation Framework of Node Contribution Based on Diffusion Information

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