A Data-Driven Graph Generative Model for Temporal Interaction Networks

Zhou, Dawei; Zheng, Lecheng; Han, Jiawei; He, Jingrui

doi:10.1145/3394486.3403082

Cited by 81 publications

(39 citation statements)

References 17 publications

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“…They may also utilize recurrent neural networks for predicting future connections or edges in the graph based on node-ordering procedures [112]. There are other techniques that utilize efficient sampling strategies to extract patterns in input data and learn their dynamics to generate a predicted temporal network [139]. Other than structural patterns of the system, estimating future states of other agents can help agents to optimize their own actions more efficiently.…”

Section: Predictive Modelsmentioning

confidence: 99%

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Nezamoddini

Gholami

2022

Smart Cities

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The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented.

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Section: Predictive Modelsmentioning

confidence: 99%

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Nezamoddini

Gholami

2022

Smart Cities

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“…TagGen is a deep graph generative model for dynamic networks [34]. In their learning process they treat the data as a temporal interaction network, where the network is represented as a collection of temporal edges and each node is associated with multiple timestamped edges at different timestamps.…”

Section: Taggenmentioning

confidence: 99%

DYMOND: DYnamic MOtif-NoDes Network Generative Model

Zeno

Fond

Neville

2021

Proceedings of the Web Conference 2021

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Motifs, which have been established as building blocks for network structure, move beyond pair-wise connections to capture longerrange correlations in connections and activity. In spite of this, there are few generative graph models that consider higher-order network structures and even fewer that focus on using motifs in models of dynamic graphs. Most existing generative models for temporal graphs strictly grow the networks via edge addition, and the models are evaluated using static graph structure metrics-which do not adequately capture the temporal behavior of the network. To address these issues, in this work we propose DYnamic MOtif-NoDes (DYMOND)-a generative model that considers (i) the dynamic changes in overall graph structure using temporal motif activity and (ii) the roles nodes play in motifs (e.g., one node plays the hub role in a wedge, while the remaining two act as spokes). We compare DYMOND to three dynamic graph generative model baselines on real-world networks and show that DYMOND performs better at generating graph structure and node behavior similar to the observed network. We also propose a new methodology to adapt graph structure metrics to better evaluate the temporal aspect of the network. These metrics take into account the changes in overall graph structure and the individual nodes' behavior over time.

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“…A systematic comparison of some of the aforementioned methodologies for unsupervised network embedding is provided in Khosla et al (2021). Methodologies have also been recently proposed in the dynamic network setting, within the context of representation learning (for example, Nguyen et al 2018;Kumar et al 2019;Liu et al 2019;Qu et al 2020), and deep generative models (for example, Zhou et al 2020). The interested reader is referred to the survey of Kazemi et al (2020) and references therein.…”

Section: Related Literaturementioning

confidence: 99%

Link prediction in dynamic networks using random dot product graphs

Passino

Bertiger

Neil

et al. 2021

Data Min Knowl Disc

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The problem of predicting links in large networks is an important task in a variety of practical applications, including social sciences, biology and computer security. In this paper, statistical techniques for link prediction based on the popular random dot product graph model are carefully presented, analysed and extended to dynamic settings. Motivated by a practical application in cyber-security, this paper demonstrates that random dot product graphs not only represent a powerful tool for inferring differences between multiple networks, but are also efficient for prediction purposes and for understanding the temporal evolution of the network. The probabilities of links are obtained by fusing information at two stages: spectral methods provide estimates of latent positions for each node, and time series models are used to capture temporal dynamics. In this way, traditional link prediction methods, usually based on decompositions of the entire network adjacency matrix, are extended using temporal information. The methods presented in this article are applied to a number of simulated and real-world graphs, showing promising results.

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A Data-Driven Graph Generative Model for Temporal Interaction Networks

Cited by 81 publications

References 17 publications

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

DYMOND: DYnamic MOtif-NoDes Network Generative Model

Link prediction in dynamic networks using random dot product graphs

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