IEEE International Conference on Computer Communications 2019 (InfoCom 2019)将于2019年4月29日至5月2日，在法国巴黎举行。InfoCom是计算机网络通信领域的国际顶级学术会议，也被CCF推荐为计算机网络方向的A类会议。
17级同学覃孟在实验室雷凯老师指导下，完成一篇长文”GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks”，并以确认被InfoCom 2019录用！中稿论文的简介如下：
论文标题: GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks
论文作者: Kai Lei, Meng Qin, Bo Bai*, Gong Zhang, Min Yang*
英文摘要: 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.
中文简介: 本文将各种网络系统的动态性预测问题(如对于移动性、流量和动态拓扑的预测)一般性地建模为时序链路预测任务。传统的时序链路预测技术往往忽略了网络的非线性特征，以及具有深层物理含义的拓扑权重。与这些传统方法不同，提出一种新的非线性模型GCN-GAN，以解决带权动态网络的时序链路预测问题。该模型结合了图卷积网络(GCN)、LSTM和生成对抗网络(GAN)各自的优势，能够充分挖掘网络的动态性、拓扑结构，以及演化模式，以此提高时序链路预测的性能。具体地，GCN首先被用于提取单个网络快照的局部拓扑特征；接着，使用LSTM特征化动态网络的演化特征。进一步地，GAN被用于增强模型的生成下一个时间片网络快照的能力，并有效地应对真实网络的稀疏性和边权值具有较大取值范围的问题。为了验证模型的有效性，在4个涉及到不同网络系统和应用场景的数据集上进行了实验。相关结果表明，该模型取得了相比于现有方法更好的性能。