Collective classification in social networks

• Published in: 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) pp. 827 - 835
• Main Authors: Jaafor, Omar, Birregah, Babiga
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 31.07.2017
• Series: ACM Conferences
• Subjects: Classification algorithms; collective classification; Data models; Human-centered computing; Human-centered computing > Collaborative and social computing; Human-centered computing > Collaborative and social computing > Collaborative and social computing systems and tools; Human-centered computing > Collaborative and social computing > Collaborative and social computing systems and tools > Social networking sites; Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms; Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms > Social media; Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms > Social networks; Human-centered computing > Human computer interaction (HCI); Human-centered computing > Human computer interaction (HCI) > Interaction paradigms; Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Web-based interaction; Information systems; Information systems > Information systems applications; Information systems > Information systems applications > Collaborative and social computing systems and tools; Information systems > Information systems applications > Collaborative and social computing systems and tools > Social networking sites; Information systems > Information systems applications > Data mining; Information systems > World Wide Web; Information systems > World Wide Web > Web applications; Information systems > World Wide Web > Web applications > Social networks; Malware; multi-layer networks; Networks; Networks > Network types; Networks > Network types > Overlay and other logical network structures; Networks > Network types > Overlay and other logical network structures > Online social networks; Prediction algorithms; Semi-supervised classification; Social network services; social networks; surveillance; Task analysis; Training; unbalanced data; Semi-supervised classification; unbalanced data; collective classification; surveillance; multi-layer networks; social networks
• ISBN: 1450349935; 9781450349932
• ISSN: 2473-991X
• DOI: 10.1145/3110025.3110128

Classification is one of the most studied subjects in machine learning. Most classification methods that were developed this last decade either account for structure (interactions, relationships) or attributes (text, numerical, etc). This leads to ignoring significant patterns in a dataset that could only be captured by analyzing the features of an item and its interactions. Collective classification methods use both structure and attributes, often by aggregating data from neighbors of a node and learning a model on the aggregated data. In social networks, the degree distribution of nodes follows a power law where few nodes have many neighbors. High degree nodes have incoming links from low degree nodes of different classes and many nodes have very few edges. Hence, using only local structure may lead to poor predictions. Also, many social networks allow for different types of interactions (retweet, reply, like, etc.) that affect classification differently. This article proposes a collective classification method that makes use of the structure of a network to determine its neighbors. It then presents experiments aimed at detecting jihadi propagandists and malware distributors on social networks.