Robust Dynamic Clustering for Temporal Networks

You, Jingyi; Hu, Chenlong; Kamigaito, Hidetaka; Funakoshi, Kotaro; Okumura, Manabu

doi:10.1145/3459637.3482473

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…The available tools for analyzing such data is often inspired by the tools from the static network community; for instance, centrality or flow measures [18]; temporal clustering for event detection [19][20][21]; and connectedness [22]. However, these tools do not account for higher-dimensional structures (e.g., loops as a one-dimensional structure).…”

Section: Introductionmentioning

confidence: 99%

Temporal network analysis using zigzag persistence

et al. 2023

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This work presents a framework for studying temporal networks using zigzag persistence, a tool from the field of Topological Data Analysis (TDA). The resulting approach is general and applicable to a wide variety of time-varying graphs. For example, these graphs may correspond to a system modeled as a network with edges whose weights are functions of time, or they may represent a time series of a complex dynamical system. We use simplicial complexes to represent snapshots of the temporal networks that can then be analyzed using zigzag persistence. We show two applications of our method to dynamic networks: an analysis of commuting trends on multiple temporal scales, e.g., daily and weekly, in the Great Britain transportation network, and the detection of periodic/chaotic transitions due to intermittency in dynamical systems represented by temporal ordinal partition networks. Our findings show that the resulting zero- and one-dimensional zigzag persistence diagrams can detect changes in the networks’ shapes that are missed by traditional connectivity and centrality graph statistics.

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Section: Introductionmentioning

confidence: 99%

Temporal network analysis using zigzag persistence

et al. 2023

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“…Furthermore, the clustering performance is undesirable due to the independence of sentence representation learning and sentence clustering. Even though structure learning has the potential to address the above issues by co-clustering on a newly created bipartite graph to extract the clustering structure (You et al, 2021a;Nie et al, 2017), it is not suitable for our framework to make H s a block-diagonal matrix with k components. Theorem 1 paves the way to detect the clustering structure of H s by adding a low-rank constraint.…”

Section: Sentence Clustering Constraintmentioning

confidence: 99%

Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization

You¹,

Li²,

Kamigaito³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event detection as two independent tasks, which makes it impossible to integrate their advantages and obtain a globally optimal summary. In this paper, we present a joint learning-based heterogeneous graph attention network for TLS (HeterTLS), in which date selection and event detection are combined into a unified framework to improve the extraction accuracy and remove redundant sentences simultaneously. Our heterogeneous graph involves multiple types of nodes, the representations of which are iteratively learned across the heterogeneous graph attention layer. We evaluated our model on four datasets, and found that it significantly outperformed the current stateof-the-art baselines with regard to ROUGE scores and date selection metrics.

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“…The standard network analysis tools for studying temporal networks often include measures such as centrality or flow measures [37], temporal clustering for event detection [38][39][40], and connectedness [41]. However, these tools do not account for higher-dimensional structures (e.g., loops as a one-dimensional structure).…”

Section: Introductionmentioning

confidence: 99%

Temporal Network Analysis Using Zigzag Persistence

Myers¹,

Khasawneh²,

Munch³

2022

Preprint

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This work presents a framework for studying temporal networks using zigzag persistence, a tool from the field of Topological Data Analysis (TDA). The resulting approach is general and applicable to a wide variety of time-varying graphs. For example, these graphs may correspond to a system modeled as a network with edges whose weights are functions of time, or they may represent a time series of a complex dynamical system. We use simplicial complexes to represent snapshots of the temporal networks that can then be analyzed using zigzag persistence. We show two applications of our method to dynamic networks: an analysis of commuting trends on multiple temporal scales, e.g., daily and weekly, in the Great Britain transportation network, and the detection of periodic/chaotic transitions due to intermittency in dynamical systems represented by temporal ordinal partition networks. Our findings show that the resulting zero-and one-dimensional zigzag persistence diagrams can detect changes in the networks' shapes that are missed by traditional connectivity and centrality graph statistics.

show abstract

Robust Dynamic Clustering for Temporal Networks

Cited by 6 publications

References 41 publications

Temporal network analysis using zigzag persistence

Temporal network analysis using zigzag persistence

Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization

Temporal Network Analysis Using Zigzag Persistence

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