A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information
A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information
复制标题
一种多维异构信息的大规模图排序方法
DOI：
10.1109/tcyb.2015.2418233
复制
发表时间：
2016-04
影响因子：
11.8
通讯作者：
Hang Li
中科院分区：
文献类型：
--
作者：
Wei Wei;Bin Gao;Tie-Yan Liu;Taifeng Wang;Guohui Li;Hang Li
基于图的排名已经被广泛研究，并经常应用于许多应用中，例如网页排名。它旨在从原始的图结构数据中挖掘潜在的有价值的信息。最近，随着丰富的异构信息（例如，节点/边特征和先验知识）在许多现实世界的图中可用，如何有效且高效地利用所有信息来提高排名性能成为一个新的挑战性问题。先前的方法仅利用这些信息的一部分，并试图根据基于链接的方法对图节点进行排名，其中排名性能受到几个众所周知的问题的严重影响，例如，过拟合或计算复杂度高，特别是当图的规模很大时。在本文中，我们解决了大规模的基于图的排名问题，并专注于如何有效地利用丰富的异构信息的图，以提高排名性能。具体来说，我们提出了一种创新且有效的半监督PageRank（SSP）方法，在统一的半监督学习框架（SSLF-GR）中参数化派生信息，然后同时优化图节点的参数和排名分数。在真实世界的大规模图上的实验表明，我们的方法显着优于只考虑部分图信息的算法。
Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.
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影响因子：
8.3
作者：
Xueyuan Zhou;M. Belkin;N. Srebro
通讯作者：
Xueyuan Zhou;M. Belkin;N. Srebro
DOI：
10.1051/m2an/196903r100351
发表时间：
1969
期刊：
--
影响因子：
--
作者：
Série Rouge;E. Polak;G. Ribière
通讯作者：
Série Rouge;E. Polak;G. Ribière
影响因子：
--
作者：
Tie-Yan Liu;Jun Xu;Tao Qin;Wen-Ying Xiong;Hang Li
通讯作者：
Tie-Yan Liu;Jun Xu;Tao Qin;Wen-Ying Xiong;Hang Li
影响因子：
11.8
作者：
Jia Wu;Shirui Pan;Xingquan Zhu;Z. Cai
通讯作者：
Jia Wu;Shirui Pan;Xingquan Zhu;Z. Cai
DOI：
10.1145/1143844.1143848
发表时间：
2006-06
期刊：
Proceedings of the 23rd international conference on Machine learning
影响因子：
--
作者：
S. Agarwal
通讯作者：
S. Agarwal