Aspect-Level Influence Discovery from Graphs
Aspect-Level Influence Discovery from Graphs
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DOI：
10.1109/tkde.2016.2538223
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发表时间：
2016-07-01
影响因子：
8.9
通讯作者：
Cao, Huiping
中科院分区：
文献类型：
--
作者：
Hu, Chuan;Cao, Huiping
图已被广泛用于表示诸如web、社交网络和引用网络之类的应用中的对象和对象连接。近年来，从图中挖掘影响关系获得了越来越多的关注，因为提供关于图对象如何相互影响的信息可以促进图探索、图搜索和连接推荐。在本文中，我们研究的问题，检测的影响方面，对象连接，影响程度（或影响强度），其中一个图节点影响另一个图节点在一个给定的方面。现有的技术集中于从图中推断整体影响程度或影响类型。在本文中，我们提出了一个系统的方法来提取影响方面和学习方面级的影响强度。特别是，我们首先提出了一种新的基于实例合并的方法，从对象连接的上下文中提取影响方面。然后，我们介绍了两个生成模型，观察方面的影响模型（OAIM）和潜在方面的影响模型（莱姆），模型的拓扑结构的图形，与图形对象相关联的文本内容，以及在其中的对象连接的上下文。为了学习OAIM和莱姆，我们设计了非并行和并行Gibbs采样算法.我们进行了广泛的实验，合成和真实的数据集，以显示我们的方法的有效性和效率。实验结果表明，我们的模型可以发现更有效的结果比现有的方法。我们的学习算法在大型数据集上也具有很好的扩展性。
Graphs have been widely used to represent objects and object connections in applications such as the web, social networks, and citation networks. Mining influence relationships from graphs has gained increasing interests in recent years because providing information on how graph objects influence each other can facilitate graph exploration, graph search, and connection recommendations. In this paper, we study the problem of detecting influence aspects, on which objects are connected, and influence degree (or influence strength), with which one graph node influences another graph node on a given aspect. Existing techniques focus on inferring either the overall influence degrees or the influence types from graphs. In this paper, we propose a systematic approach to extract influence aspects and learn aspect - level influence strength. In particular, we first present a novel instance - merging based method to extract influence aspects from the context of object connections. We then introduce two generative models, Observed Aspect Influence Model (OAIM) and Latent Aspect Influence Model (LAIM), to model the topological structure of graphs, the text content associated with graph objects, and the context in which the objects are connected. To learn OAIM and LAIM, we design both non - parallel and parallel Gibbs sampling algorithms. We conduct extensive experiments on synthetic and real data sets to show the effectiveness and efficiency of our methods. The experimental results show that our models can discover more effective results than existing approaches. Our learning algorithms also scale well on large data sets.