Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Abstract

The establishment of connections among social network users using their profile information is an important task in social network analysis, which facilitates the development of various technological solutions such as stock market analysis, crime detection, tracking system of fraudulent events, etc. In this work, a proximity-based clustering method for networking LinkedIn profiles is presented. The proposed system computes proximity value between users using various attributes of user profiles. The proximity measures are computed by analyzing unstructured data of user profiles to connect users. The method addresses various issues such as comparison of familiar sentences, finding patterns, and sub-patterns among user profiles, assigning weights on attributes similarity, and computing total similarity which is associated with unstructured data. After computing proximity measures on various attributes of user profiles, the connecting edges between nodes are determined by employing artificial intelligence and a network graph is formed. The method is evaluated on a LinkedIn data-set to form a connected graph. The strength of the proposed methodology lies in the formation of multi-layered network graphs, as it uses various attributes of the user profiles to connect them. The proposed methodology helps various applications like recommendation systems to form network graphs of selected attributes and perform the social network analysis. The method achieves an accuracy of 96%. However, the profiles containing abbreviations of important information are not matched and the system accuracy drops down in such cases.

QST-ClustNet: Quantum Inspired Sooty Tern Optimization for Clustering User Profiles in Social Network

“…Such methods base their analysis of social networks and community detection on node structural similarity. On the other hand, clustering is a technique where a set of instances are divided into smaller groups known as clusters, with each cluster's members being very similar to one another, and quite dissimilar from those of the other clusters based on the node features or attributes [6][7][8]. A user's profile information frequently includes some node traits, such as age, gender, and interests on several social networks like Facebook, and Twitter.…”