An Influence Maximization Algorithm Based on Community-Topic Features for Dynamic Social Networks

Qin, Xi; Zhong, Cheng; Yang, Qingkai

doi:10.1109/tnse.2021.3127921

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…Taking into account the influence of topics, [11] proposed a novel topicaware influence-driven dissemination model. [12] introduced a topic-aware community-based independent cascade model. [13] presented a topic-aware social influence dissemination model.…”

Section: Related Workmentioning

confidence: 99%

“…[14] proposed NSTI-IC model. Accounting for the influence of spatial factors, [12] introduced a communitybased independent cascade model. Taking into account the influence of individual preference factors, [14] proposed the NSTI-IC model.…”

Section: Related Workmentioning

confidence: 99%

“…Kempe et al proposed the Independent Cascade (IC) Model [4] and the Linear Threshold (LT) Model [5]. Considering the influence of topic, Qin et al introduced a topic-aware community-based Model that incorporated both the structural characteristics of communities and thematic features [6]. [7] considered the influence of similar topics between nodes and proposed a new model.…”

Section: Related Workmentioning

confidence: 99%

“…Existing social network information dissemination model research is mainly based on two classic models: the Independent Cascade (IC) Model [3] and the Linear Threshold (LT) Model [4]. For example, based on the IC model, researchers have carried out a large number of studies considering the influence of time [5][6][7], user emotions [8][9][10], topics [11][12][13][14], and individual preferences [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Later researchers found that apart from the neighboring nodes considered by the aforementioned models, additional factors also exert influence on the information dissemination process. Consequently, a substantial body of research has emerged, which incorporated different elements, e.g., userinterest topics [6][7][8][9], individual preferences [9][10][11], infor-Fig. 1: Traditional Network Model.…”

Section: Introductionmentioning

confidence: 99%

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Volumetric video, also known as hologram video, is a novel medium that portrays natural content in Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). It is expected to be the next-gen video technology and a prevalent use case for 5G and beyond wireless communication. Considering that each user typically only watches a section of the volumetric video, known as the viewport, it is essential to have precise viewport prediction for optimal performance. However, research on this topic is still in its infancy. In the end, this paper presents and proposes a novel approach, named Saliency and Trajectory Viewport Prediction (STVP), which aims to improve the precision of viewport prediction in volumetric video streaming. The STVP extensively utilizes video saliency information and viewport trajectory. To our knowledge, this is the first comprehensive study of viewport prediction in volumetric video streaming. In particular, we introduce a novel sampling method, Uniform Random Sampling (URS), to reduce computational complexity while still preserving video features in an efficient manner. Then we present a saliency detection technique that incorporates both spatial and temporal information for detecting static, dynamic geometric, and color salient regions. Finally, we intelligently fuse saliency and trajectory information to achieve more accurate viewport prediction. We conduct extensive simulations to evaluate the effectiveness of our proposed viewport prediction methods using state-of-the-art volumetric video sequences. The experimental results show the superiority of the proposed method over existing schemes. The dataset and source code will be publicly accessible after acceptance.

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Section: Related Workmentioning

confidence: 99%