GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

by   Kai Lei, et al.

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.


page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8


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

Predicting the potential relations between nodes in networks, known as l...

Temporal Link Prediction via Adjusted Sigmoid Function and 2-Simplex Sructure

Temporal network link prediction is an important task in the field of ne...

A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism

Short-term traffic flow prediction is a vital branch of the Intelligent ...

TSAM: Temporal Link Prediction in Directed Networks based on Self-Attention Mechanism

The development of graph neural networks (GCN) makes it possible to lear...

A source separation approach to temporal graph modelling for computer networks

Detecting malicious activity within an enterprise computer network can b...

Handwriting Prediction Considering Inter-Class Bifurcation Structures

Temporal prediction is a still difficult task due to the chaotic behavio...

Generative Temporal Link Prediction via Self-tokenized Sequence Modeling

We formalize networks with evolving structures as temporal networks and ...

Please sign up or login with your details

Forgot password? Click here to reset