A Novel Emerging Topic Identification and Evolution Discovery Method on Time‐Evolving and Heterogeneous Online Social Networks

Abstract

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.

Comparação entre a detecção de comunidades e o algoritmo K-Means na descoberta do espectro do Transtorno do Déficit de Atenção e Hiperatividade (TDAH)

“…Essas duas técnicas têm sido aplicadas em diferentes domínios. O k-means tem sido utilizado em diversos campos, como detecc ¸ão dinâmica de comunidades em redes sociais online [34], classificac ¸ão de habilidades de estudantes [36], soluc ¸ões de recuperac ¸ão para nuvens híbridas [38], gerenciamento de conhecimento por meio do agrupamento de documentos [39] e detecc ¸ão robusta de comunidades em grafos [40]. Essas aplicac ¸ões destacam a versatilidade do algoritmo k-means na identificac ¸ão de clusters e comunidades em vários conjuntos de dados.…”