Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

نویسندگان

  • Junxing Zhu
  • Jiawei Zhang
  • Quanyuan Wu
  • Yan Jia
  • Bin Zhou
  • Xiaokai Wei
  • Philip S. Yu
چکیده

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Link Prediction across Heterogeneous Social Networks: A Survey

Online social networks have gained great success in recent years. Some online social networks only involving users and social links among users can be represented as homogeneous networks. Meanwhile, some other social networks containing abundant information, which include multiple kinds of nodes and complex relationships, can be denoted as heterogeneous networks. Predicting the missing links or...

متن کامل

A Link Prediction Method Based on Learning Automata in Social Networks

Nowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probability of creation of a new relationship in future. A wide range of applications can be found for link prediction such as electro...

متن کامل

Integrated Anchor and Social Link Predictions across Social Networks

To enjoy more social network services, users nowadays are usually involved in multiple online social media sites at the same time. Across these social networks, users can be connected by both intranetwork links (i.e., social links) and inter-network links (i.e., anchor links) simultaneously. In this paper, we want to predict the formation of social links among users in the target network as wel...

متن کامل

Transfer Link Prediction across Heterogeneous Social Networks

Interpersonal ties are responsible for the structure of social networks and the transmission of information through these networks. Different types of social ties have essentially different influence on people. Awareness of the types of social ties can benefit many applications such as recommendation and community detection. For example, our close friends tend to move in the same circles that w...

متن کامل

Link Prediction in Heterogeneous Collaboration Networks

Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this article, we study both supervised and unsupervised link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heteroge...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره 17  شماره 

صفحات  -

تاریخ انتشار 2017