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10 articles for “Social Network Metrics”
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Finding Influential Users in Online Social Networks: A Tree based Approach
Abstract: In online social networking, people are getting involved in communications, but the real problem that comes to the mind is whether these communications can bring revolutions, and if so, who are the best people in given social networks that are really holding the remote control i.e. they have the power to bring these revolutions. This problem and the likes can be modelled as detecting influential users in online social networks. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 3, 2018 · pp. 66–70 Read article
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Overview of Various Dynamic Community Evolution Detection Approaches
Abstract: Social network in a simplest form is a social structure consisting of units that are connected by various kinds of relations like friendship, common interest, financial exchange, dislike, knowledge or prestige. This can be represented by a combination of graph theory and data mining concepts. The easiest way to present social network in a mathematical way is graph representation where members are nodes of the graph and relations are edges …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 3, Issue 1, 2015 · pp. 28–32 Read article
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Centrality in Social Network Analysis: A Comprehensive Review for Students
Abstract: Centrality, a fundamental concept in social network analysis (SNA), plays a pivotal role in understanding the structural dynamics and information flow within networks. This studyoffers an extensive examination of centrality metrics and their utilization in diverse fields. We begin by defining centrality and exploring its significance in characterizing the importance of nodes within a network. Subsequently, we delve into the literature review by different authors and most employed centrality metrics, …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 2, 2023 · pp. 1–8 Read article
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Terrorist Network Mining
Abstract: AbstractTerrorist activities worldwide has moved toward the advancement of different high-finished strategies for dissecting psychological militant gatherings and disrupting government systems. Existing examination found that social network analysis (SNA) is a standout amongst the best and persistent technique for countering fear mongering in interpersonal organizations. In this paper our approach is divided in two phases, in Phase 1, we apply shuffled frog leap algorithm (SFLA) for SNA in order to …
Published in Journal of Communication Engineering & Systems · Vol. 7, Issue 2, 2017 · pp. 42–52 Read article
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Analysis on Link Prediction Algorithm for Social Network
Abstract: AbstractThe paper presents the analysis on link prediction algorithm for social network. Link prediction is one of the essential problems in computational social science. A principally general means to predict subsistence of unnoticed links is via structural similarity metrics, such as the number of common neighbors; node pairs with higher similarity are thus deemed more expected to be linked. There have been many algorithms to solve for the link prediction …
Published in Current Trends in Signal Processing · Vol. 10, Issue 1, 2020 · pp. 1–7 Read article
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Application of Resource Allocation Similarity Based Link Prediction in Wireless Networks
Abstract: Link prediction in wireless networks plays a crucial role in predicting missing connections within multiplex networks. This study focuses on the utilization of similarity-based link prediction methods in wireless networks. These methods assume that the likelihood of linkage between nodes is determined by their similarity, based on shared features. Several similarity measures, such as Common Neighbors (CN), Preferential Attachment (PA), Adamic-Adar (AA), and Resource Allocation (RA) indices, are commonly employed …
Published in International Journal of Wireless Security and Networks · Vol. 1, Issue 2, 2023 · pp. 37–42 Read article
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AI-Driven Psychological Profiling on Social Media: Mechanisms, Ethical Breaches, and Regulatory Challenges in Data Inference
Abstract: This literature review examines AI-driven psychological profiling on social media, analyzing 21 academic studies that focus on machine learning techniques such as supervised learning, deep neural networks, sentiment analysis, and natural language processing. These methodologies infer mental health indicators—such as depression, anxiety, and stress—from users' digital footprints, encompassing linguistic patterns, engagement metrics, and temporal behaviors. While these tools offer potential for early detection of psychological distress, they also raise significant …
Published in Recent Trends in Social Studies · Vol. 2, Issue 1, 2025 · pp. 1–7 Read article
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Examining the Crowd in Real-time with Deep Learning
Abstract: In this research, a model is proposed that can be used to estimate crowd density in a specific region and to establish social distances in accordance with predetermined rules. This is accomplished utilizing a multi-source model-based approach. In a small public gathering where hand counting is impossible, this technique conducts a survey. To do this, input video frames are extracted, each frame is processed, and then passed to the model …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 1, 2023 · pp. 41–45 Read article
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Intrinsic Evaluation of Graph Embeddings: Assessing Clustering and Community Detection Performance
Abstract: This paper presents an intrinsic evaluation of some graph embedding techniques on clustering and community detection tasks. We analyze a diverse set of embedding methods, ranging from traditional techniques such as Laplacian eigenmaps to more recent approaches like graph autoencoders, high-order proximity preserved embedding (HOPE), and graph attention network (GAT), using two widely studied datasets, Cora and CiteSeer. Our evaluation relies on two main metrics: Silhouette score with respect to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 40–48 Read article
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Hybrid Approach for Community Detection Using Deep Learning Techniques
Abstract: Community detection in complex networks is a fundamental problem with applications across diverse domains, ranging from social networks to biological systems and beyond. Traditional methods based on graph theory have been widely used for identifying communities within networks. However, the intricate and evolving nature of modern networks demands more sophisticated approaches. This research work proposes a hybrid approach that combines the strengths of deep learning techniques with traditional community detection …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 3, 2024 · pp. 18–26 Read article