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.
Highlights
In recent years, with the fast development of web 2.0, social network sites such as Facebook, Sina microblog, and Twitter rise in a short time, a huge heterogeneous online social networks have gradually formed on which the functional role of online users is changing from the information consumers to both diffusers and generators [1]
Aiming to tackle these problems, we propose an emerging topic identification and evolution topology discovery framework based on a novel dynamic community detection method on the time-evolving and heterogeneous social network
We denote and name it as KeyGraph G {Vi, Eij} in the following way, where i, j represent the ith and jth short texts crawled from heterogeneous social networks and marked with a number, Ci is the keyword set of the ith short text using word segmentation technology, Nij is the count number of common keywords belonging to keyword sets of both Ci and Cj; Vi is the ith node of the network, Eij represents the edge between the ith and jth short texts which is closely related to the common keywords number Nij. e relationship of Eij with Nij is shown in the following formula: Eij 1, if Nij > 0, (1)
Summary
With the fast development of web 2.0, social network sites such as Facebook, Sina microblog, and Twitter rise in a short time, a huge heterogeneous online social networks have gradually formed on which the functional role of online users is changing from the information consumers to both diffusers and generators [1]. When applying existing community detection methods for time-evolving and heterogeneous networks, three main problems are usually encountered: (1) most existed community detection methods are proposed for the static and homogeneous network; (2) the semantic relationships and dynamic properties of communities are violently damaged and even bluntly lost due to the man-made segmentation of network; (3) a great amount of computing time and space cost is required by storing the historical community structure information as the initial input values Aiming to tackle these problems, we propose an emerging topic identification and evolution topology discovery framework based on a novel dynamic community detection method on the time-evolving and heterogeneous social network.
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