CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization DOI Creative Commons
Chen Dong, Guiqiong Xu,

Lei Meng

et al.

Chinese Physics B, Journal Year: 2024, Volume and Issue: 33(8), P. 088901 - 088901

Published: June 3, 2024

Abstract The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation various rumors. In order to block outbreak rumor, one most effective containment measures is spreading positive information counterbalance diffusion rumor. mechanism rumors suppression strategies are significant challenging research issues. Firstly, in simulate dissemination multiple types information, we propose competitive linear threshold model with state transition (CLTST) describe process rumor anti-rumor same network. Subsequently, put forward community-based blocking (CRB) algorithm based on influence maximization theory networks. Its crucial step identify set influential seeds that propagate other nodes, which includes community detection, selection candidate generation seed set. Under CLTST model, CRB has been compared six state-of-the-art algorithms nine verify performance. Experimental results show proposed can better reflect propagation, review Moreover, performance weakening ability, select more accurately achieve spread, sensitivity analysis, distribution running time.

Language: Английский

Locating influential nodes in hypergraphs via fuzzy collective influence DOI

S. F. Zhang,

Xiaoyan Yu, Gui‐Quan Sun

et al.

Communications in Nonlinear Science and Numerical Simulation, Journal Year: 2025, Volume and Issue: unknown, P. 108574 - 108574

Published: Jan. 1, 2025

Language: Английский

Citations

1

Deep reinforcement learning-based influence maximization for heterogeneous hypergraphs DOI

Yanhao Sun,

Jie Wu,

Nuan Song

et al.

Physica A Statistical Mechanics and its Applications, Journal Year: 2025, Volume and Issue: 660, P. 130361 - 130361

Published: Jan. 9, 2025

Language: Английский

Citations

1

UHIR: An effective information dissemination model of online social hypernetworks based on user and information attributes DOI Creative Commons
Yunchao Gong,

Min Wang,

Liang Wei

et al.

Information Sciences, Journal Year: 2023, Volume and Issue: 644, P. 119284 - 119284

Published: June 15, 2023

With the expansion of number users in online social networks, diversity and community characteristics become more prominent. Hypernetwork theory provides a path for characterizing complex relationships networks. This paper used hypergraph's hyperedges to represent relationship between users, created an hypernetwork information dissemination model (UHIR model) based on user attributes by combining with SEIR model. Through this model, article simulated analyzed dynamic process laws under different network structures, studied influence influence, confidence, interest value, timeliness process. The simulation results show that can accurately describe trend real network. work extends new research direction hypernetworks contributes in-depth study mechanisms.

Language: Английский

Citations

19

Social contagions on higher-order community networks DOI
Jiachen Li, Wenjie Li,

Feng Gao

et al.

Applied Mathematics and Computation, Journal Year: 2024, Volume and Issue: 478, P. 128832 - 128832

Published: May 22, 2024

Language: Английский

Citations

9

Influence maximization on hypergraphs via multi-hop influence estimation DOI Creative Commons

Xulu Gong,

Hanchen Wang, Xiaoyang Wang

et al.

Information Processing & Management, Journal Year: 2024, Volume and Issue: 61(3), P. 103683 - 103683

Published: Feb. 15, 2024

Influence Maximization (IM) has promising applications in social network marketing and been extensively researched over the past years. However, previous IM studies mainly focus on ordinary graphs rather than hypergraphs, where edges cannot accurately describe group interactions or relationships. To model interactions, we investigate problem hypergraphs under Susceptible–Infected spreading with Contact Process dynamics (SICP) this paper. In paper, proposed a probability distribution-based method, called Multi-hop Estimation (MIE), which can estimate rank of influence expectation nodes, to solve hypergraphs. Specifically, compute score for each node through constrained Depth First Search (DFS) model, then select seed according score. addition, by analysing characteristics diffusion find that is significantly related its neighbourhood structure. Based observation, propose term named coefficient structure node. Further, an efficient effective Adaptive Neighbourhood Coefficient Algorithm (Adeff), Extensive experiments real-world datasets demonstrate effectiveness efficiency our methods. Compared state-of-the-art approach, methods achieve up 450% improvement terms effectiveness.

Language: Английский

Citations

8

Influential simplices mining via simplicial convolutional networks DOI
Yujie Zeng, Yiming Huang, Qiang Wu

et al.

Information Processing & Management, Journal Year: 2024, Volume and Issue: 61(5), P. 103813 - 103813

Published: June 29, 2024

Language: Английский

Citations

5

Message-passing approach to higher-order percolation DOI
Hao Peng, Cheng Qian, Dandan Zhao

et al.

Physica A Statistical Mechanics and its Applications, Journal Year: 2023, Volume and Issue: 634, P. 129446 - 129446

Published: Dec. 19, 2023

Language: Английский

Citations

13

HEDV-Greedy: An Advanced Algorithm for Influence Maximization in Hypergraphs DOI Creative Commons
Haosen Wang, Qingtao Pan, Jun Tang

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(7), P. 1041 - 1041

Published: March 30, 2024

Influence maximization (IM) has shown wide applicability in various fields over the past few decades, e.g., viral marketing, rumor control, and prevention of infectious diseases. Nevertheless, existing research on IM primarily focuses ordinary networks with pairwise connections between nodes, which fall short representation higher-order relations. hypergraphs (HIM) received limited attention. A novel evaluation function, aims to evaluate spreading influence selected nodes hypergraphs, i.e., expected diffusion value hypergraph (HEDV), is proposed this work. Then, an advanced greedy-based algorithm, termed HEDV-greedy, select seed maximum hypergraph. We conduct extensive experiments eight real-world datasets, benchmarking HEDV-greedy against state-of-the-art methods for HIM problem. Extensive conducted datasets highlight effectiveness efficiency our methods. The algorithm demonstrates a marked reduction time complexity by two orders magnitude compared conventional greedy method. Moreover, outperforms other algorithms across all datasets. Specifically, under conditions lower propagation probability, exhibits average improvement solution accuracy 25.76%.

Language: Английский

Citations

4

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy DOI Creative Commons
Feng Hu, Kuo Tian, Zi-Ke Zhang

et al.

Entropy, Journal Year: 2023, Volume and Issue: 25(9), P. 1263 - 1263

Published: Aug. 25, 2023

Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph method (HVC) that integrates as well its optimized version (semi-SAVC). HVC is based on the line graph structure hypergraphs measures changes network complexity using entropy. It s-line quantify importance by mapping hyperedges nodes. In contrast, semi-SAVC uses quadratic approximation entropy measure considers only half maximum order hypergraph's balance accuracy efficiency. Compared baseline methods hyperdegree centrality, closeness vector sub-hypergraph new demonstrated superior nodes promote influence maintain connectivity empirical data, considering robustness factors. The correlation monotonicity results were quantitatively analyzed comprehensive experimental demonstrate superiority methods. At same time, key non-trivial phenomenon was discovered: does not increase linearly orders increase. We call this saturation effect identification. When reaches value, addition often acts noise affects propagation.

Language: Английский

Citations

11

Influence maximization based on threshold models in hypergraphs DOI Open Access
Renquan Zhang, Xilong Qu, Qiang Zhang

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2024, Volume and Issue: 34(2)

Published: Feb. 1, 2024

Influence maximization problem has received significant attention in recent years due to its application various domains, such as product recommendation, public opinion dissemination, and disease propagation. This paper proposes a theoretical analysis framework for collective influence hypergraphs, focusing on identifying set of seeds that maximize threshold models. First, we extend the message passing method from pairwise networks hypergraphs accurately describe activation process Then, introduce concept hypergraph (HCI) measure nodes. Subsequently, design an algorithm, HCI-TM, select set, taking into account both node hyperedge activation. Numerical simulations demonstrate HCI-TM outperforms several competing algorithms synthetic real-world hypergraphs. Furthermore, find HCI can be used tool predict occurrence cascading phenomena. Notably, algorithm works better larger average hyperdegrees Erdös–Rényi smaller power-law exponents scale-free

Language: Английский

Citations

4