Transfer Learning to Infer Social Ties across Heterogeneous Networks

Abstract

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 influences 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 we do, while our classmates may be distributed into different communities. Though a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of predicting social ties across multiple heterogeneous networks. In this work, we develop a framework referred to as TranFG for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a factor graph model, which effectively improves the accuracy of predicting the types of social relationships in a target network by borrowing knowledge from a different source network. We also present several active learning strategies to further enhance the inferring performance. To scale up the model to handle really large networks, we design a distributed learning algorithm for the proposed model. We evaluate the proposed framework (TranFG) on six different networks and compare with several existing methods. TranFG clearly outperforms the existing methods on multiple metrics. For example, by leveraging information from a coauthor network with labeled advisor-advisee relationships, TranFG is able to obtain an F1-score of 90% (8%--28% improvements over alternative methods) for predicting manager-subordinate relationships in an enterprise email network. The proposed model is efficient. It takes only a few minutes to train the proposed transfer model on large networks containing tens of thousands of nodes.

BayDNN

“…First, it is interesting to study incrementally learning the deep neural networks so that we can involve online user feedback into the learning process. Second, another potential is to infer the ned category (e.g., family, friend, and colleague) of social relationships [40]. Last, it would be interesting to connect the study with social theories to further understand how the topological structure of online social networks is formed.…”

“…e classi cation methods are based on extracted features, e.g., features between two nodes like path-based metric Katz [14] or neighbor-based metric Adamic/Adar [23]. By regarding the friendship status between two users as a binary classi cation task, they train classi ers such as SVM [8], logistic regression [29], and factor graph [40] to predict the friendship values between users. e ing methods represent the friendship between users bu real values and try to approximate the values for observed friendship as close as they can, e.g., [12,25] used matrix factorization to predict the value of unknown friendship.…”

Inferring Advisor‐Student Relationships from Publication Networks Based on Approximate MaxConfidence Measure

“…The MAN is further divided into three subdatasets: Teacher, PhD, and Colleague. Teacher contains both graduated students and graduate students pairing with Input: = ( , , ) Output: = {( , , ) , ∈ } (1) Initialize all as 0, as 0, as 0, respectively; (2) For each ∈ do (3) update and according to 's publication list; (4) for each ∈ do (5) update and according to 's publication list; (6) check the restriction R2 according to 's and 's publication history; (7) if restriction R2 is valid then (8) decide who the potential advisor is according to and ; (9) determinte the start year based on 's and 's publication list; (10) calculate all , and year by year; (11) determinte the end year ; (12) calculate the relationship score of advisor-student relationship; (13) select whose is maximum as 's advisor; their advisors, while PhD only contains advisor-PhD pairs. Colleague is a negative dataset for our experiments, which contains coauthor or colleague pairs.…”

“…Zhuang et al [7] precisely define the problem of inferring social ties and propose a Partially Labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social relationships. Tang et al [8,9] and He et al [10] present a framework for classifying the type of social relationships by learning across heterogeneous networks, respectively. These proposed algorithms are based on factor graph and are computation-intensive.…”

On relationship formation in heterogeneous information networks: An inferring method based on multilabel learning

“…These networks can be regarded as specific HINs, either with multiple node types or with multiple link types. Recently, there are more and more interests in link analysis and prediction in complex HINs [6,10,15,18,20,30,32,34,36,37] as most of networks in real world are heterogeneous in nature.…”