A user interest community evolution model based on subgraph matching for social networking in mobile edge computing environments

Abstract

With the rapid development of mobile edge computing, mobile social networks are gradually infiltrating into our daily lives, in which the communities are an important part of social networks. Internet of People such as online social networks is the next frontier for the Internet of Things. The combination of social networking and mobile edge computing has an important application value and is the development trend of future networks. However, how to detect evolutionary communities accurately and efficiently in dynamic heterogeneous social networks remains a fundamental problem. In this paper, a novel User Interest Community Evolution (UICE) model based on subgraph matching is proposed for accurately detecting the corresponding communities in the evolution of the user interest community. The community evolutionary events can be quickly captured including forming, dissolving, evolving and so on with the introduction of core subgraph. A variant of subgraph matching, called Subgraph Matching with Dynamic Weight (SMDW), is proposed to solve the problem of updating the core subgraph due to the change of core user’s interest when tracking evolutionary communities. Finally, the experiments based on the real datasets have been designed to evaluate the performance of the proposed model by comparing it with the state-of-art methods in this area and complete data processing through the local edge computing layer. The experimental results demonstrate that the UICE model presented in this paper has achieved better accuracy, higher efficiency and better scalability against existing methods.

DIEET: Knowledge–Infused Event Tracking in Social Media based on Deep Learning

“…The time interval can be divided into the time interval of repeated transmission and the time interval of sequential transmission. In addition, according to the theory of memory trace decline in diffusion psychology [19], Therefore, this paper considers the following four factors that affect the evolution of interest.…”

“…Within this paper, referring to the subgraph increment method proposed by Jiang et al [19], a novel User Interest Community Evolution, named UICE model based on cosine similarity is introduced in order to accurately detect the corresponding communities in the evolution of the user interest community. The steps included are as Fig.…”

“…Although researchers and related institutions have made significant progress in social network topic identification [16][17][18] and tracking technology of social network information [19,20], research on event traceability is recognised as still relatively limited. Most research on event tracking and traceability technology in social networks focuses on microblog topics, this involves tracking the subsequent topic development utilising known hot topics, rather than backtracking the propagation path of a specific microblog.…”