ACM SIGCOMM 2018 Workshop on Network Meets AI & ML (NetAIM 2018)
ACM SIGCOMM 2018(https://conferences.sigcomm.org/sigcomm/2018/) (CCF A)将于2018年8月20日至25日在匈牙利布达佩斯举行，SIGCOMM是计算机网络通信领域的重要学术会议，且今年为NetAI Workshop(https://conferences.sigcomm.org/sigcomm/2018/workshop-netaim.html)在SIGCOMM会议举办的第一届，该workshop主要关注如何利用人工智能技术有效应对现有网络系统的面临的挑战。
17级同学覃孟，在实验室雷老师的指导下，完成一篇长文，并已被SIGCOMM 2018 NetAI Workshop录用，论文简介如下：
标题: Adaptive Multiple Non-negative Matrix Factorization for Temporal Link Prediction in Dynamic Networks
作者: Kai Lei, Meng Qin, Bo Bai*, Gong Zhang.
英文摘要: The prediction of mobility, topology and traffic is an effective technique to improve the performance of various network systems, which can be generally represented as the temporal link prediction problem. In this paper, we propose a novel adaptive multiple nonnegative matrix factorization (AM-NMF) method from the view of network embedding to cope with such problem. Under the framework of non-negative matrix factorization (NMF), the proposed method embeds the dynamic network into a low-dimensional hidden space, where the characteristics of different network snapshots are comprehensively preserved. Especially, our new method can effectively incorporate the hidden information of different time slices, because we introduce a novel adaptive parameter to automatically adjust the relative contribution of different terms in the uniform model. Accordingly, the prediction result of future network topology can be generated by conducting the inverse process of NMF form the shared hidden space. Moreover, we also derive the corresponding solving strategy whose convergence can be ensured. As an illustration, the new model will be applied to various network datasets such as human mobility networks, vehicle mobility networks, wireless mesh networks and data center networks. Experimental results show that our method outperforms state-of-the-art methods for the temporal link prediction of both unweighted and weighted networks.
中文简介：对于用户移动性、网络动态拓扑和网络流量的预测是改善各种网络系统性能的有效手段，而相关的网络系统动态性预测问题能够以复杂网络分析的观点一般性地抽象为时序链路预测(temporal link prediction)问题。从网络表征(network embedding)的观点出发，提出一种自适应多重非负矩阵分解(adaptive nonnegative matrix factorization, AM-NMF)模型解决上述问题。在非负矩阵分解(nonnegative matrix factorization, NMF)框架下，该模型将动态网络嵌入到一个保留了不同网络快照动态变化特征的低维隐含空间。特别地，由于引入自适应参数自动地调节混合模型中不同分量的相对重要性，该模型还能有效地结合不同时间片下的隐含信息，并考虑单个时间片与动态网络整体的内在关联性。进一步地，关于下一个时间片网络快照的预测结果能够通过执行NMF的逆过程生成。作为一个应用示例，该模型也被应用于各种网络系统相关的数据集，包括人移动网络、车辆移动网络、无线网格网络和数据中心网络。相关实验结果表明，该方法在无权网络和带权网络的时序链路预测任务上的性能超过现有的方法。
Knowledge-Based Systems 期刊
Knowledge-Based Systems(https://www.journals.elsevier.com/knowledge-based-systems/) 是人工智能领域跨学科、面向应用的学术期刊，最新的影响因子(IF)为4.396。
17级同学覃孟，在实验室雷老师的指导下，完成一篇论文，并已确认被Knowledge-Based Systems期刊录用，论文具体简介如下:
标题: Adaptive Community Detection Incorporating Topology and Content in Social Networks
作者: Meng Qin, Di Jin*, Kai Lei*, Bogdan Gabrys, Katarzyna Musial
引用格式:
@article{Qin2018Adaptive,
title={Adaptive community detection incorporating topology and content in social networks},
author={Qin, Meng and Jin, Di and Lei, Kai and Gabrys, Bogdan and Musialgabrys, Katarzyna},
journal={Knowledge Based Systems},
year={2018}
}
论文下载链接: https://authors.elsevier.com/c/1X~J83OAb8tDQD
部分核心源代码下载链接: https://github.com/KuroginQin/ASCD
英文摘要: In social network analysis, community detection is a basic step to understand the structure and function of networks. Some conventional community detection methods may have limited performance because they merely focus on the networks’ topological structure. Besides topology, content information is another significant aspect of social networks. Although some state-of-the-art methods started to combine these two aspects of information for the sake of the improvement of community partitioning, they often assume that topology and content carry similar information. In fact, for some examples of social networks, the hidden characteristics of content may unexpectedly mismatch with topology. To better cope with such situations, we introduce a novel community detection method under the framework of non- negative matrix factorization (NMF). Our proposed method integrates topology as well as content of networks and has an adaptive parameter (with two variations) to effectively control the contribution of content with respect to the identified mismatch degree. Based on the disjoint community partition result, we also introduce an additional overlapping community discovery algorithm, so that our new method can meet the application requirements of both disjoint and overlapping community detection. The case study using real social networks shows that our new method can simultaneously obtain the community structures and their corresponding semantic description, which is helpful to understand the semantics of communities. Related performance evaluations on both artificial and real networks further indicate that our method outperforms some state-of-the-art methods while exhibiting more robust behavior when the mismatch between topology and content is observed.
中文简介: 在社交网络分析中，社团发现(community detection)是理解网络结构和功能的基本步骤。一些传统的社团发现方法由于只考虑网络的拓扑结构，社团划分的性能受到限制。除了网络拓扑，内容信息是社交网络另一个重要方面。为进一步提升社团划分的准确性，现有的一些方法也开始结合网络拓扑和内容，然而这些方法均假设拓扑和内容具有相似的隐含特征。实际上，对于一些真实社交网络，内容信息的隐含特征可能与拓扑结构不匹配(mismatch)。为更好地应对上述问题，基于非负矩阵分解(non-negative matrix factorization, NMF)框架提出一种新的社团发现方法。该方法结合了网络的拓扑结构和内容信息两个方面，并引入一个基于不匹配程度(mismatch degree)的自适应参数来控制内容信息在混合模型中的相对作用。在非重叠社团划分(disjoint community detection)的基础上，还提出了一种重叠社团发现(overlapping community detection)算法，因此该模型能够同时应对非重叠和重叠社团发现两种应用场景。在真实社交网络数据集上的一个实例分析也表明，该方法具有同时完成网络社团划分并生成社团语义描述的能力，而生成的语义描述能够有效地帮助理解网络社团结构的语义。在人工网络和真实社交网络上的相关性能分析也表明，该方法相对于现有方法具有更好的社团发现性能；并且在网络拓扑与内容不匹配较严重的情况下，表现出了更强的鲁棒性。