Predicting Communication Intention in Social Networks

C. Chelmis, V. Prasanna
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引用次数: 12

Abstract

In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate micro logging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.
预测社交网络中的交际意图
在社交网络中,用户互相发送信息,是什么触发了不相关用户之间的交流:以前不相关的用户之间的交流是否依赖于朋友之间的关系类型、共同兴趣或其他因素?在这项工作中,我们研究了两个用户之间直接通信意图的预测问题。链路预测与通信意图类似,都是利用网络结构进行预测。然而,这两个问题由于关注点不同而表现出根本性的差异。链接预测使用证据来预测网络结构演变,而我们的重点是以前没有结构连接的用户之间的定向通信启动。为了解决这个问题,我们将拓扑证据与交易信息结合起来,以预测通信意图。在这种情况下,用于链接预测的方法是否也能很好地工作,这并不直观。事实上,我们在这项工作中表明,当单独考虑时,网络或内容证据并不是足够准确的预测因素。我们的新方法联合考虑了社交网络中用户的局部结构属性,以及他们生成的内容,捕获了大量的直接和间接的、社交的和上下文的交互,这些交互迄今为止都是独立考虑的。我们进行了一项实证研究,利用提取的企业微日志服务(类似于Twitter)用户之间发送的定向@消息网络来评估我们的方法。我们发现我们的方法优于最先进的链接预测技术。我们的研究结果对广泛的社交网络应用程序具有启示意义,例如问答的上下文专家推荐,新的友谊关系的创建和有针对性的内容交付。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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