OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation
OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation
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DOI：
10.1609/aaai.v29i1.9555
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
Yuto Yamaguchi;C. Faloutsos;H. Kitagawa
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文献类型：
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作者：
Yuto Yamaguchi;C. Faloutsos;H. Kitagawa
如果我们知道史密斯的大多数朋友都来自波士顿，那么我们对史密斯的其他朋友有什么看法呢？在本文中，我们专注于网络上的节点分类问题，这是人工智能和Web社区中最重要的话题之一。我们提出的被称为OMNIProp的算法具有以下特性：（a）无缝和准确;它在任何标签相关性上都能很好地工作（即，同质性、异质性和它们的混合）（B）快速;它是有效的，并保证收敛到任意图上（c）无准参数;它只有一个启发式默认值为1的可解释参数。我们还证明了我们的算法的半监督学习（SSL）算法和随机行走的理论连接。四个真实的，不同的网络数据集上的实验证明了所提出的算法，其中OMNI-Prop优于顶级竞争对手的好处。
If we know most of Smith’s friends are from Boston, what can we say about the rest of Smith’s friends? In this paper, we focus on the node classification problem on networks, which is one of the most important topics in AI and Web communities. Our proposed algorithm which is referred to as OMNIProp has the following properties: (a) seamless and accurate; it works well on any label correlations (i.e., homophily, heterophily, and mixture of them) (b) fast; it is efficient and guaranteed to converge on arbitrary graphs (c) quasi-parameter free; it has just one well-interpretable parameter with heuristic default value of 1. We also prove the theoretical connections of our algorithm to the semi-supervised learning (SSL) algorithms and to random-walks. Experiments on four real, different network datasets demonstrate the benefits of the proposed algorithm, where OMNI-Prop outperforms the top competitors.