User interest community detection on social media using collaborative filtering

Jiang, Liang; Shi, Leilei; Liu, Lu; Yao, Jingjing; Yousuf, Muhammad Ali

doi:10.1007/s11276-018-01913-4

Cited by 30 publications

(38 citation statements)

References 17 publications

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“…Hence, in order to be able to select the core node set in a short period of time, the impact of the largest solution in the selection of community classification algorithm in addition to the time complexity of the low, but also should be stable and reasonable and community detection of quality assurance. This paper proposes a stable and high-quality algorithm based on node influence called HLPA [30]. To be more specific, the HLPA algorithm assigns a unique interest label to each user node, and then updates the user node's interest label in descending order of interest popularity.…”

Section: Mining Initial Influential Spreadersmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Mining Initial Influential Spreadersmentioning

confidence: 99%

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

et al. 2020

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“…Algorithms based on network dynamics apply the dynamic propagation laws of complex networks to discover communities by dividing the community structure based typically on the different propagation characteristics exhibited within and between communities. Typical dynamic community detection algorithms are the label propagation algorithm (Jiang et al 2019;Jokar and Mosleh 2019;You et al 2020) and random walk algorithm (Raghavan et al 2007;vanDongen 2000).…”

Section: Related Workmentioning

confidence: 99%

Community detection based on similarities of communication behavior in IP networks

Zhang

Zhou

et al. 2020

J Ambient Intell Human Comput

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Communities are an important feature of real-world networks that can reveal the structure and dynamic characteristics of networks. Accordingly, the accurate detection and analysis of the community structure in large-scale IP networks is highly beneficial for their optimization and security management. This paper addresses this issue by proposing a novel community detection method based on the similarity of communication behavior between IP nodes, which is determined by analyzing the communication relationships and frequency of interactions between the nodes in the network. On this basis, the nodes are iteratively added to the community with the highest similarity to form the final community division result. The results of experiments involving both complex public network datasets and real-world IP network datasets demonstrate that the proposed method provides superior community detection performance compared to that of four existing state-of-the-art community detection methods in terms of modularity and normalized mutual information indicators.

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“…Besides, topic keyword set will be created by extracting keywords from users' interests [7], as well as from the key posts of users, which point to hot events [17]. Finally, target user prediction set will be achieved by calculating topic similarity between the content of all detected hot events and the content of all detected users' interests [7,9,10] and employing those scores in user prediction process. These sets are composed of the intelligent event propagation model's experience sets.…”

Section: Intelligent Event Propagation Processmentioning

confidence: 99%

“…In recent years, online social network management has become an important part of our daily lives [1][2][3][4][5]. As a form of online social network management, microblogging network management platforms are also developing and attracting people at a rapid pace [6][7][8][9][10]. Microblogging network management platforms is known as the best tool for people to share and exchange opinion [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Event Detection and Multi-source Propagation for Online Social Network Management

Shi

Liu

et al. 2019

J Netw Syst Manage

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The social network is a huge source of information, which plays an increasingly crucial role in people's daily lives. As a form of online social network management, much information can be discovered via posts, which allows people to exchange and propagate real-life events. Multi-source event propagation involves relevant posts of interesting topics from some key users to others in microblogging network users for network management. However, there are many noisy data in traditional microblogging network management. Meanwhile few people study the spontaneous transmission of events in microblogging network management, as well as the cooperation and competition among multiple event sources. To this end, the event detection and multi-source propagation model, is established. Specifically, for efficient and accurate result of the hot event detection and propagation, we obtain the information of previous event detection and propagation to create some experience sets for the intelligent event propagation. And a multi-source events propagation model based on individual interest is established to describe the process of multi-source event information detection and dissemination, and to describe the key role of users and information characteristics in the process of communication and network management. Meanwhile, the experimental results show that the proposed intelligent multisource events detection and propagation model can learn from previous propagation to better discover and propagate the hot events under users' changing interest. Besides, the interaction broadens the influence scope of hot events. This helps to explain the formation of microblogging hot events dissemination, to provide a theoretical basis for the research and network management of the guiding strategy.

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User interest community detection on social media using collaborative filtering

Cited by 30 publications

References 17 publications

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

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

Community detection based on similarities of communication behavior in IP networks

Event Detection and Multi-source Propagation for Online Social Network Management

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