Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part V
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part V
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数据库中的机器学习和知识发现 - 欧洲会议，ECML PKDD 2022，法国格勒诺布尔，2022 年 9 月 19-23 日，会议记录，第五部分
DOI：
10.1007/978-3-031-26419-1_12
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Naik C
中科院分区：
文献类型：
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作者：
Naik C
在本文中，我们提出了一个贝叶斯非参数方法来建模稀疏时变网络。一个正参数与网络的每个节点相关联，它模拟了该节点的社交性。社会性被认为是随着时间的推移而发展，并通过一个动态的点过程模型建模。该模型能够捕捉社会性的长期演变。此外，它产生稀疏图，其中边的数量随着节点的数量次二次增长。社会性的演变是由一个易处理的随时间变化的广义伽马过程。我们提供了一些理论见解的模型，并将其应用到三个数据集：一个模拟的网络，Reddit上的社区之间的超链接网络，并在路透社的新闻文章后，9月袭击的词的共同占用的网络。
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over time, and are modelled via a dynamic point process model. The model is able to capture long term evolution of the sociabilities. Moreover, it yields sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying generalised gamma process. We provide some theoretical insights into the model and apply it to three datasets: a simulated network, a network of hyperlinks between communities on Reddit, and a network of co-occurences of words in Reuters news articles after the Septemberattacks.