Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks

Zhu, Linhong; Guo, Dong; Yin, Jun; Steeg, Greg Ver; Galstyan, Aram

doi:10.1109/tkde.2016.2591009

Cited by 237 publications

(159 citation statements)

References 42 publications

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“…To validate the effectiveness of our E-LSTM-D model, we compare it with node2vec, as a widely used baseline network embedding method, as well as four state-of-the-art DNLP methods that could handle time dependencies, including Temporal Network Embedding (TNE) [41], conditional temporal RBM (ctRBM) [29], Gradient boosting decision tree based Temporal RBM (GTRBM) [42] and Deep Dynamic Network Embedding (DDNE) [31]. In particular, the five baselines are introduced as follows.…”

Section: B Baseline Methodsmentioning

confidence: 99%

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Chen

Zhang

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

128

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Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science. However, most studies just focused on link prediction of static network, while real-world networks always evolve over time with the occurrence and vanishing of nodes and links. Dynamic network link prediction thus has been attracting more and more attention since it can better capture the evolution nature of networks, but still most algorithms fail to achieve satisfied prediction accuracy. Motivated by the excellent performance of Long Short-Term Memory (LSTM) in processing time series, in this paper, we propose a novel Encoder-LSTM-Decoder (E-LSTM-D) deep learning model to predict dynamic links end to end. It could handle long term prediction problems, and suits the networks of different scales with finetuned structure. To the best of our knowledge, it is the first time that LSTM, together with an encoder-decoder architecture, is applied to link prediction in dynamic networks. This new model is able to automatically learn structural and temporal features in a unified framework, which can predict the links that never appear in the network before. The extensive experiments show that our E-LSTM-D model significantly outperforms newly proposed dynamic network link prediction methods and obtain the state-of-the-art results.

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Section: B Baseline Methodsmentioning

confidence: 99%

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Chen

Zhang

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

128

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“…Others studied network evolution [24], knowledge graph dynamics [47], and information cascades on Facebook and Twitter [6,23]. Temporal graph behavior has been studied in several directions: some works [22,27,29,58] focused on temporal prediction problems where the input is a graph. They studied numerous deep-learning approaches for representing an entire graph and minimizing a loss function for a specific prediction task.…”

Section: Related Workmentioning

confidence: 99%

Node Embedding over Temporal Graphs

Singer

Guy

Radinsky

2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

115

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In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.

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“…We vary α from {0.001, 0.01, 0.1}. • TNE [36] is a dynamic network embedding model based on matrix factorization. We set λ with a grid search from {0.01, 0.1, 1}.…”

Section: Baselinesmentioning

confidence: 99%

Temporal Network Embedding with Micro- and Macro-dynamics

Wang

Shi

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopic and macroscopic dynamics, are ubiquitous. The microdynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale. Both micro-and macro-dynamics are the key factors to network evolution; however, how to elegantly capture both of them for temporal network embedding, especially macro-dynamics, has not yet been well studied. In this paper, we propose a novel temporal network embedding method with micro-and macro-dynamics, named M 2 DNE. Specifically, for micro-dynamics, we regard the establishments of edges as the occurrences of chronological events and propose a temporal attention point process to capture the formation process of network structures in a fine-grained manner. For macro-dynamics, we define a general dynamics equation parameterized with network embeddings to capture the inherent evolution pattern and impose constraints in a higher structural level on network embeddings. Mutual evolutions of micro-and macro-dynamics in a temporal network alternately affect the process of learning node embeddings. Extensive experiments on three real-world temporal networks demonstrate that M 2 DNE significantly outperforms the state-of-the-arts not only in traditional tasks, e.g., network reconstruction, but also in temporal tendency-related tasks, e.g., scale prediction. Definition 3.3. Macro-dynamics. Given a temporal network G = (V, E, T ), macro-dynamics refer to the evolution process of the network scale, denoted as A = {e(t)} | T | t =t 1 , where e(t) is the cumulative number of edges by time t.

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Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks

Cited by 237 publications

References 42 publications

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Node Embedding over Temporal Graphs

Temporal Network Embedding with Micro- and Macro-dynamics

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