Socio-spatial affiliation networks
Socio-spatial affiliation networks
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DOI：
10.1016/j.comcom.2015.06.002
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
K. Pelechrinis;P. Krishnamurthy
中科院分区：
文献类型：
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作者：
K. Pelechrinis;P. Krishnamurthy
基于位置的社交网络（LBSNs）最近吸引了大量的关注，由于他们可以提供的新服务的数量。以前的工作分析LBSNs主要集中在这些系统的社会部分。尽管知道LBSN的社交图的结构与基于友谊的社交网络（SN）相比有多大不同是很重要的，但它提出了一个有趣的问题，即位置和友谊之间存在什么样的联系。我们正在调查的主要问题是，以确定这样的LBSN的社会和空间平面之间的连接。特别是，在本文中，我们专注于回答以下一般性的问题“什么是社会和空间信息之间的债券在LBSN和什么是可以揭示他们的指标？”为了解决这个问题，我们采用了隶属网络的思想。分析数据集从一个特定的LBSN（Gowalla），我们做了两个主要的有趣的观察;（i）的社交网络oblitsssigns同性恋与用户访问的“地方/场所”，以及（ii）的“性质”的访问场所，是共同的用户是强大的和翔实的揭示社会/空间的联系。我们进一步表明，“熵”的场地可以用来更好地连接空间信息与现有的社会关系。熵记录了场地的多样性，并且只需要用户的位置历史（它不需要时间历史）。最后，我们提供了一个简单的应用，我们的研究结果预测现有的友谊关系的基础上，用户的历史空间信息。我们表明，即使使用简单的无监督或有监督学习模型，当我们考虑捕捉场地“性质”的特征时，与仅使用位置历史的明显属性的情况相比，我们也可以在预测方面取得显着改进（例如，常见访问次数）。
Location-based social networks (LBSNs) have recently attracted a lot of attention due to the number of novel services they can offer. Prior work on analysis of LBSNs has mainly focused on the social part of these systems. Even though it is important to know how different the structure of the social graph of an LBSN is as compared to the friendship-based social networks (SNs), it raises the interesting question of what kinds of linkages exist between locations and friendships. The main problem we are investigating is to identify such connections between the social and the spatial planes of an LBSN. In particular, in this paper we focus on answering the following general question “What are the bonds between the social and spatial information in an LBSN and what are the metrics that can reveal them?” In order to tackle this problem, we employ the idea ofaffiliation networks. Analyzing a dataset from a specific LBSN (Gowalla), we make two main interesting observations; (i) the social network exhibitssigns of homophilywith regards to the “places/venues” visited by the users, and (ii) the “nature” of the visited venues that are common to users is powerful and informative in revealing the social/spatial linkages. We further show that the “entropy” of a venue can be used to better connect spatial information with the existing social relations. The entropy records the diversity of a venue and requires only location history of users (it does not need temporal history). Finally, we provide a simple application of our findings for predicting existing friendship relations based on users’ historic spatial information. We show that even with simple unsupervised or supervised learning models we can achieve significant improvement in prediction when we consider features that capture the “nature” of the venue as compared to the case where only apparent properties of the location history are used (e.g., number of common visits).