Maximizing the Influence in Dynamic Social Networks: An Entropy-Based Linear Threshold Model

计算机科学 最大化 节点(物理) 熵(时间箭头) 动态网络分析 阈值 熵最大化 过程(计算) 社交网络(社会语言学) 选择(遗传算法) 网络结构 最大熵原理 数学优化 人工智能 理论计算机科学 数学 计算机网络 结构工程 操作系统 图像(数学) 物理 量子力学 工程类 万维网 社会化媒体
作者
Yi Li,Jianyong Yu,Yuqi Liu,Hangyu Zhu
标识
DOI:10.1109/cscloud-edgecom54986.2022.00028
摘要

Various communication methods allow modern people to communicate with each other more frequently, resulting in a more diverse network structure in today’s social network due to its own dynamics. Compared with the influence maximization under the static network, which has been carried out a lot of related research, the dynamic network’s influence maximization has more practical significance. The dynamic network, which is closer to the evolution of real social network, becomes more challenging for its influence maximization problem due to the variability of its network structure. In this paper, the traditional thresholding model is improved to make it feasible to calculate the threshold at each node and make the model can be used in dynamic networks. By proposing related theorems, we propose a new node seed selection strategy, which can select new nodes that are beneficial to the propagation between nodes by calculating the node influence. Through comparative experiments with five different algorithms, we select two real datasets with large structural differences to generate directed and weighted dynamic networks. The experimental results show that the propagation range caused by our method during the process and at the end of the propagation is much larger than that of other comparison algorithms.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
乖猫要努力应助momo采纳,获得10
刚刚
诚心问筠完成签到,获得积分10
刚刚
云泥完成签到,获得积分10
2秒前
2秒前
隐形曼青应助okface采纳,获得10
3秒前
zhang完成签到,获得积分10
4秒前
hopen完成签到 ,获得积分10
5秒前
丘比特应助SS采纳,获得10
7秒前
月亮moon完成签到,获得积分10
7秒前
8秒前
充电宝应助qqq采纳,获得10
11秒前
顾矜应助lihan采纳,获得10
11秒前
爆米花应助周em12_采纳,获得10
12秒前
13秒前
三物完成签到 ,获得积分10
14秒前
jolt发布了新的文献求助10
15秒前
17秒前
17秒前
18秒前
momo发布了新的文献求助10
19秒前
量子星尘发布了新的文献求助30
19秒前
20秒前
21秒前
21秒前
善学以致用应助笃定采纳,获得30
22秒前
SS发布了新的文献求助10
24秒前
24秒前
ZZZ发布了新的文献求助10
24秒前
Justtry发布了新的文献求助20
26秒前
26秒前
顾矜应助momo采纳,获得10
27秒前
27秒前
hnlgdx发布了新的文献求助20
27秒前
29秒前
重要冷之完成签到,获得积分20
29秒前
29秒前
32秒前
VV完成签到,获得积分10
32秒前
ccalvintan发布了新的文献求助10
33秒前
蛋挞发布了新的文献求助10
34秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989297
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253893
捐赠科研通 3270097
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809158