An insight into topological, machine and Deep Learning-based approaches for influential node identification in social media networks: a systematic review

Rashid, Yasir; Bhat, Javaid Iqbal

doi:10.1007/s00530-023-01258-9

Cited by 2 publications

(1 citation statement)

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“…The rapid development of communication, information dissemination and global social interaction has made social networks an integral part of our daily lives [ 1 ]. A social network comprises a complex network of connections between nodes, typically visualized as a graph, where nodes represent users or entities and edges describe the relationship or interaction between them.…”

Section: Introductionmentioning

confidence: 99%

Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model

Huang,

Li,

Yuan

2024

Entropy

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Link prediction is recognized as a crucial means to analyze dynamic social networks, revealing the principles of social relationship evolution. However, the complex topology and temporal evolution characteristics of dynamic social networks pose significant research challenges. This study introduces an innovative fusion framework that incorporates entropy, causality, and a GCN model, focusing specifically on link prediction in dynamic social networks. Firstly, the framework preprocesses the raw data, extracting and recording timestamp information between interactions. It then introduces the concept of “Temporal Information Entropy (TIE)”, integrating it into the Node2Vec algorithm’s random walk to generate initial feature vectors for nodes in the graph. A causality analysis model is subsequently applied for secondary processing of the generated feature vectors. Following this, an equal dataset is constructed by adjusting the ratio of positive and negative samples. Lastly, a dedicated GCN model is used for model training. Through extensive experimentation in multiple real social networks, the framework proposed in this study demonstrated a better performance than other methods in key evaluation indicators such as precision, recall, F1 score, and accuracy. This study provides a fresh perspective for understanding and predicting link dynamics in social networks and has significant practical value.

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Section: Introductionmentioning

confidence: 99%