GRADE: Graph Dynamic Embedding

Spasov, S; Stefano, Alessandro Di; Lió, Píetro; Tang, Jian

doi:10.48550/arxiv.2007.08060

Cited by 3 publications

(5 citation statements)

References 37 publications

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“…One direct real-world application of THVMs could be to serve as null models [65,181] for evolving networks with dynamic node-properties [75]. Dynamic embedding methods [142][143][144][145][146][147][148][149][150][151][152][153][154], or generalizations of inference methods from dynamic SBMs [73], could potentially allow retrieval of H (and perhaps also σ, ω, and f ) from an observed G. Links of real evolving networks may not in general be fully equilibrated relative to the current set of nodecharacteristics, which is a dynamical behavior exhibited by THVMs outside of the quasi-static regime. Hence in some cases, the Equilibrium Property and Qualitative Realism may be in conflict, implying that caution should be used when applying static models to snapshots of evolving networks.…”

Section: Discussionmentioning

confidence: 99%

“…Another study was of a temporal hyper-SBM with ω < 1 which thus exhibits both link-persistence and group-assignment-persistence [73], influencing performance of community detection algorithms and motivating the development of new ones. Another area of relevant work is the rapidly emerging area of dynamic graph embeddings [75,[142][143][144][145][146][147][148][149][150][151][152][153][154], related to the task of inference of hidden-variable trajectories [155].…”

Section: Related Workmentioning

confidence: 99%

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Dynamic Hidden-Variable Network Models

Hartle,

Papadopoulos,

Krioukov

2021

Preprint

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Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Realworld networks evolve over time, and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The rates of the hidden variable dynamics and link dynamics are controlled by two parameters, and snapshots of networks in the dynamic models may or may not be equivalent to a static model, depending on the location in the parameter phase diagram. We quantify deviations from static-like behavior, and examine the level of structural persistence in the considered models. We explore temporal versions of popular static models with community structure, latent geometry, and degree-heterogeneity. We do not attempt to directly model real networks, but comment on interesting qualitative resemblances, discussing possible extensions, generalizations, and applications.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dynamic Hidden-Variable Network Models

Hartle,

Papadopoulos,

Krioukov

2021

Preprint

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“…Average node activity. Average node activity quantifies the average proportion of timestamps during which a node is present within a temporal network [9]. This metric reveals the degree of temporal activity of nodes.…”

Section: Temporal Graph Statisticsmentioning

confidence: 99%

Temporal Graph Analysis with TGX

Shirzadkhani,

Huang,

Kooshafar

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

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Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely designed for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs. TGX provides access to eleven built-in datasets and eight external Temporal Graph Benchmark (TGB) datasets as well as any novel datasets in the .csv format. Beyond data loading, TGX facilitates data processing functionalities such as discretization of temporal graphs and node subsampling to accelerate working with larger datasets. For comprehensive investigation, TGX offers network analysis by providing a diverse set of measures, including average node degree and the evolving number of nodes and edges per timestamp. Additionally, the package consolidates meaningful visualization plots indicating the evolution of temporal patterns, such as Temporal Edge Appearance (TEA) and Temporal Edge Traffic (TET) plots. The TGX package is a robust tool for examining the features of temporal graphs and can be used in various areas like studying social networks, citation networks, and tracking user interactions. We plan to continuously support and update TGX based on community feedback. TGX is publicly available on: https://github.com/ComplexData-MILA/TGX. CCS CONCEPTS• Networks → Network algorithms; Network structure.

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“…Some works embed the graph convolution into the recurrent neural network (RNN) based models or attention mechanism [33], which learns to exploit the dynamic information in the graph evolution within a period of time [13], [14], [16], [17], [34], [35], [36]. Some other works are dynamic extensions of ideas applied in the static case inspired by methods such as PageRank [37], graph autoencoder [38] and the topic model [15] to capture both the temporal community dynamics and evolution of graph structure. However, learning embedding from the sequence of graph snapshots sampled from the temporal graph by a regular time interval may lose information by looking only at some snapshots of the graph over time.…”

Section: Temporal Graph Embeddingmentioning

confidence: 99%

“…Recently, representation learning on dynamic graph has attracted many research attention [10], [11], [12], and they mainly model temporal graphs either as a sequence of snapshots [13], [14], [15], [16], [17], [18], or as real events with timestamps [19], [20], [21], [22], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

ConTIG: Continuous Representation Learning on Temporal Interaction Graphs

Xu¹,

Fan²,

Yang³

et al. 2021

Preprint

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Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems. Existing dynamic embedding methods on TIG discretely update node embeddings merely when an interaction occurs. They fail to capture the continuous dynamic evolution of embedding trajectories of nodes. In this paper, we propose a two-module framework named ConTIG, a continuous representation method that captures the continuous dynamic evolution of node embedding trajectories. With two essential modules, our model exploit three-fold factors in dynamic networks which include latest interaction, neighbor features and inherent characteristics. In the first update module, we employ a continuous inference block to learn the nodes' state trajectories by learning from time-adjacent interaction patterns between node pairs using ordinary differential equations. In the second transform module, we introduce a self-attention mechanism to predict future node embeddings by aggregating historical temporal interaction information. Experiments results demonstrate the superiority of ConTIG on temporal link prediction, temporal node recommendation and dynamic node classification tasks compared with a range of state-of-the-art baselines, especially for long-interval interactions prediction.

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GRADE: Graph Dynamic Embedding

Cited by 3 publications

References 37 publications

Dynamic Hidden-Variable Network Models

Dynamic Hidden-Variable Network Models

Temporal Graph Analysis with TGX

ConTIG: Continuous Representation Learning on Temporal Interaction Graphs

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