An Efficient Evolutionary User Interest Community Discovery Model in Dynamic Social Networks for Internet of People

Jiang, Liang; Shi, Leilei; Liu, Lu; Yao, Jingjing; Yuan, Bo; Zheng, Yongjun

doi:10.1109/jiot.2019.2893625

Cited by 45 publications

(35 citation statements)

References 39 publications

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“…The data obtained from social networks is disorganized and faces the problem of sparse data and cold start, which requires pre-processing the data to improve the recommendation model. To this end, this paper introduces the HITS algorithm [32] to the recommendation model.…”

Section: Data Preprocessingmentioning

confidence: 99%

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

et al. 2020

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The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculating K nearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.

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Section: Data Preprocessingmentioning

confidence: 99%

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

et al. 2020

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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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“…The main purpose here is to achieve a personalised ranking of the predicted nodes that can satisfy the given query. The query is augmented with the query node's content and structure information to represent its query embedding, where the assumption is based on the following experimental studies: (1) similar nodes tend to interact with nodes who share common characteristics [22] [23], and (2) node's interests and local attributes can enhance the query's efficiency in fully decentralised environments [24] [25]. Fig.…”

Section: Algorithm 2: Semantic Proximity Resource Discovery (Sprd)mentioning

confidence: 99%

An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence

Yuan

Panneerselvam

Liu

et al. 2019

IEEE Trans. Ind. Inf.

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Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effective resource discovery services in edge environments, by achieving a proximity-preserving representation of the network resources. Most of the existing techniques of network embedding fail to capture accurate proximity information among the network nodes and further lack exploiting information beyond the second-order neighbourhood. This paper leverages artificial intelligence for network representation and proposes a deep learning model, named Inductive Content Augmented Network Embedding (ICANE), which integrates the network structure and resource content attributes into a feature vector. Secondly, a hierarchical aggregation approach is introduced to explicitly learn the network representation through sampling the nodes and aggregating features from the higher-order neighbourhood. A semantic proximity search model is then designed to generate the top-k ranking of relevant nodes using the learned network representation. Experiments conducted on real-world datasets demonstrate the superiority of the proposed model over the existing popular methods in terms of resource discovery and the query resolving performance.

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An Efficient Evolutionary User Interest Community Discovery Model in Dynamic Social Networks for Internet of People

Cited by 45 publications

References 39 publications

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

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

An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence

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