Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

A Comprehensive Survey on Transfer Learning

Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information, and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.

A Survey on Network Embedding

3D printing has the potential to significantly change the field of microfluidics. The ability to fabricate a complete microfluidic device in a single step from a computer model has obvious attractions, but it is the ability to create truly three dimensional structures that will provide new microfluidic capability that is challenging, if not impossible to make with existing approaches. This critical review covers the current state of 3D printing for microfluidics, focusing on the four most frequently used printing approaches: inkjet (i3DP), stereolithography (SLA), two photon polymerisation (2PP) and extrusion printing (focusing on fused deposition modeling). It discusses current achievements and limitations, and opportunities for advancement to reach 3D printing's full potential.

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3D printed microfluidic devices: enablers and barriers

Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous information networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous information network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.

A Survey of Heterogeneous Information Network Analysis

The International HapMap Consortium. The International HapMap Project (Co-PI of Hong Kong Centre which responsible for 2.5% of genome)

Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels — voice call, short message, and email — to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a “rich club” maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.

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Inferring social status and rich club effects in enterprise communication networks.

It is well known that different types of social ties have essentially different influence on people. However, users in online social networks rarely categorize their contacts into "family", "colleagues", or "classmates". While 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 inferring social ties over multiple heterogeneous networks. In this work, we develop a framework 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 inferring the type of social relationships in a target network by borrowing knowledge from a different source network. Our empirical study on five different genres of networks validates the effectiveness of the proposed framework. For example, by leveraging information from a coauthor network with labeled advisor-advisee relationships, the proposed framework is able to obtain an F1-score of 90% (8-28% improvements over alternative methods) for inferring manager-subordinate relationships in an enterprise email network.

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Inferring social ties across heterogenous networks

We study the extent to which the formation of a two-way relationship can be predicted in a dynamic social network. A two-way (called reciprocal) relationship, usually developed from a one-way (parasocial) relationship, represents a more trustful relationship between people. Understanding the formation of two-way relationships can provide us insights into the micro-level dynamics of the social network, such as what is the underlying community structure and how users influence each other. Employing Twitter as a source for our experimental data, we propose a learning framework to formulate the problem of reciprocal relationship prediction into a graphical model. The framework incorporates social theories into a machine learning model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a dynamic network. Our study provides strong evidence of the existence of the structural balance among reciprocal relationships. In addition, we have some interesting findings, e.g., the likelihood of two "elite" users creating a reciprocal relationships is nearly 8 times higher than the likelihood of two ordinary users. More importantly, our findings have potential implications such as how social structures can be inferred from individuals' behaviors.

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Who will follow you back?: reciprocal relationship prediction

The theory of structural holes suggests that individuals would benefit from filling the "holes" (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information diffusion. However, there is still lack of a principled methodology to detect structural hole spanners from a given social network. In this work, we precisely define the problem of mining top-k structural hole spanners in large-scale social networks and provide an objective (quality) function to formalize the problem. Two instantiation models have been developed to implement the objective function. For the first model, we present an exact algorithm to solve it and prove its convergence. As for the second model, the optimization is proved to be NP-hard, and we design an efficient algorithm with provable approximation guarantees. We test the proposed models on three different networks: Coauthor, Twitter, and Inventor. Our study provides evidence for the theory of structural holes, e.g., 1% of Twitter users who span structural holes control 25% of the information diffusion on Twitter. We compare the proposed models with several alternative methods and the results show that our models clearly outperform the comparison methods. Our experiments also demonstrate that the detected structural hole spanners can help other social network applications, such as community kernel detection and link prediction. To the best of our knowledge, this is the first attempt to address the problem of mining structural hole spanners in large social networks.

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Mining structural hole spanners through information diffusion in social networks

We study how links are formed in social networks. In particular, we focus on investigating how a reciprocal (two-way) link, the basic relationship in social networks, is developed from a parasocial (one-way) relationship and how the relationships further develop into triadic closure, one of the fundamental processes of link formation.We first investigate how geographic distance and interactions between users influence the formation of link structure among users. Then we study how social theories including homophily, social balance, and social status are satisfied over networks with parasocial and reciprocal relationships. The study unveils several interesting phenomena. For example, “friend's friend is a friend” indeed exists in the reciprocal relationship network, but does not hold in the parasocial relationship network.We propose a learning framework to formulate the problems of predicting reciprocity and triadic closure into a graphical model. We demonstrate that it is possible to accurately infer 90p of reciprocal relationships in a Twitter network. The proposed model also achieves better performance (p20--30p in terms of F1-measure) than several alternative methods for predicting the triadic closure formation.

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Tiancheng Lou

Papers

Inferring social status and rich club effects in enterprise communication networks.

Inferring social ties across heterogenous networks

Who will follow you back?: reciprocal relationship prediction

Mining structural hole spanners through information diffusion in social networks

Learning to predict reciprocity and triadic closure in social networks