Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

Abstract

The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models

“…This analytical approach reveals insights into community dynamics, market trends, and political movements, and finds practical applications in varied areas, ranging from influence marketing to crisis management and public health surveillance. The primary objectives of this analysis include the detection of communities [2,47], the prediction of potential links [3,45,46], and crucially, the identification of influential actors [8], thus demonstrating its versatility and relevance in contemporary society.…”

Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models

“…This analytical approach reveals insights into community dynamics, market trends, and political movements, and finds practical applications in varied areas, ranging from influence marketing to crisis management and public health surveillance. The primary objectives of this analysis include the detection of communities [ 2 , 3 ], the prediction of potential links [ 4 , 5 , 6 ], and crucially the identification of influential actors [ 7 ], thus demonstrating its versatility and relevance in contemporary society.…”