多属性多关系网络数据的多标签集体分类

Priyesh Vijayan, Shivashankar Subramanian, Balaraman Ravindran
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引用次数: 9

摘要

经典的机器学习技术假设数据是id的,但现实世界的数据本质上是关系的,通常可以使用图或图表示的一些变体来表示。对关系数据建模的重要性从它在许多领域中日益增加的存在可见:电信网络、WWW、社会网络、组织网络、图像、蛋白质序列等。根据所处理的问题和提出的解决方案的性质,这一领域最近在不同的主题下受到了各种社区的广泛关注。集体分类是一种流行的方法,它使用局部分类器将节点自身属性和邻居信息嵌入到特征向量中,并在迭代过程中对节点进行分类。尽管越来越受欢迎,但多标签场景下的多属性数据集和多关系(MAMR)网络并没有受到太多关注。在MAMR数据中,节点可以使用多种类型的属性(属性视图)来表示,节点之间有多种链接类型。例如,在Twitter中,用户可以使用他们的tweet、共享的url、标签和列表成员来表示。不同的Twitter用户可以通过跟随者、跟随和转发链接来连接。其次,在许多网络中,节点与多个标签相关联。例如,Twitter用户可以使用集合L中的一个或多个标签进行标记,其中L包含用户可能喜欢的各种电影类型。受此启发,我们提出了一种多标签集体分类的学习技术,该技术使用多关系网络数据的多个属性视图来捕获属性/关系类型内部和之间的复杂标签相关性。我们在Twitter和MovieLens数据集上对我们提出的方法进行了实证评估,结果表明它比最先进的方法表现得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-label collective classification in multi-attribute multi-relational network data
Classical machine learning techniques assume the data to be i.i.d., but the real world data is inherently relational and can generally be represented using graphs or some variants of a graph representation. The importance of modeling relational data is evident from its increasing presence in many domains: Telecom networks, WWW, social networks, organizational networks, images, protein sequences, etc. This field has recently been receiving a lot of attention in various communities under different themes depending on the problem addressed and the nature of solution proposed. Collective classification is one such popular approach which involves the use of a local classifier that embeds the node's own attributes and neighbors' information in a feature vector, and classifies the nodes in an iterative procedure. Despite the increasing popularity, there is not much attention paid towards datasets with multiple attributes and multi-relational (MAMR) networks under multi-label scenarios. In MAMR data, nodes can be represented using multiple types of attributes (attribute views) and there are multiple link types between the nodes. For example, in Twitter, users can be represented using their tweets, urls shared, hashtags and list memberships. And different Twitter users can be connected using follower, followed by and re-tweet links. Secondly, in many networks, nodes are associated with more than one label. For instance, Twitter users can be tagged with one or more labels from a set L, where L contains various movie genres that a user might like. Motivated by this, we propose a learning technique for multi-label collective classification using multiple attribute views on multi-relational network data which captures complex label correlations within and across attribute/relationship types. We empirically evaluate our proposed approach on Twitter and MovieLens datasets, and we show that it performs better than the state-of-art approaches.
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