GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks
Kai Lei,Meng Qin,Bo Bai,Gong Zhang,Min Yang +4 more
- pp 388-396
TLDR
A novel non-linear model GCN-GAN is introduced to tackle the challenging temporal link prediction task of weighted dynamic networks and achieves impressive results compared to the state-of-the-art competitors.Abstract:
In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g., the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks. The proposed model leverages the benefits of the graph convolutional network (GCN), long short-term memory (LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics, topology structure and evolutionary patterns of weighted dynamic networks can be fully exploited to improve the temporal link prediction performance. Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, which can effectively tackle the sparsity and the wide-value-range problem of edge weights in real-life dynamic networks. To verify the model’s effectiveness, we conduct extensive experiments on four datasets of different network systems and application scenarios. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.read more
Citations
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Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
TL;DR: This work establishes a foundation of dynamic networks with consistent, detailed terminology and notation and presents a comprehensive survey of dynamic graph neural network models using the proposed terminology.
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IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks
Shuokang Huang,Kai Lei +1 more
TL;DR: A novel Imbalanced Generative Adversarial Network (IGAN) to tackle the class imbalance problem is proposed, and an IGAN-based Intrusion Detection System is established to cope with class-imbalanced intrusion detection.
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GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction.
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NUMFabric: Fast and Flexible Bandwidth Allocation in Datacenters
Kanthi Nagaraj,Sandeep Chinchali,Dinesh Bharadia,Hongzi Mao,Mohammadreza Alizadeh Attar,Sachin Katti +5 more
TL;DR: In this paper, the authors present xFabric, a datacenter transport design that provides flexible and fast bandwidth allocation control, which enables operators to specify how bandwidth is allocated among contending flows to optimize for different service level objectives such as minimizing flow completion times, weighted allocations, different notions of fairness, etc.
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Generative Adversarial Networks for Spatio-temporal Data: A Survey
Nan Gao,Hao Xue,Wei Shao,Sichen Zhao,Kyle Kai Qin,Arian Prabowo,Mohammad Saiedur Rahaman,Flora D. Salim +7 more
TL;DR: A comprehensive review of the recent developments of GANs for spatio-temporal data and the common practices for evaluating the performance of spatio/temporal applications with GAns is conducted.
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