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User Profiling in an Ego Network: Co-profiling Attributes and Relationships

机译：自我网络中的用户概要分析：共同概要分析属性和关系

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User attributes, such as occupation, education, and location, are important for many applications. In this paper, we study the problem of profiling user attributes in social network. To capture the correlation between attributes and social connections, we present a new insight that social connections are discriminatively correlated with attributes via a hidden factor - relationship type. For example, a user's colleagues are more likely to share the same employer with him than other friends. Based on the insight, we propose to co-profile users' attributes and relationship types of their connections. To achieve co-profiling, we develop an efficient algorithm based on an optimization framework. Our algorithm captures our insight effectively. It iteratively profiles attributes by propagation via certain types of connections, and profiles types of connections based on attributes and the network structure. We conduct extensive experiments to evaluate our algorithm. The results show that our algorithm profiles various attributes accurately, which improves the state-of-the-art methods by 12%.
机译：用户属性，例如职业，教育程度和位置，对于许多应用程序来说都很重要。在本文中，我们研究了在社交网络中分析用户属性的问题。为了捕获属性和社交关系之间的相关性，我们提出了一种新的见解，即社交关系通过隐藏的因素（关系类型）与属性有区别地相关。例如，与其他朋友相比，用户的同事更可能与他共享同一雇主。基于此见解，我们建议共同描述用户的属性及其连接的关系类型。为了实现协同分析，我们基于优化框架开发了一种有效的算法。我们的算法有效地捕获了我们的见解。它通过通过某些类型的连接传播来迭代地配置属性，并根据属性和网络结构来配置连接的类型。我们进行了广泛的实验来评估我们的算法。结果表明，我们的算法可以准确地对各种属性进行配置，从而使最新方法的性能提高了12％。

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