CSNE : Conditional Signed Network Embedding

Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the construction of useful low-dimensional representations (embeddings) of these networks for the prediction of missing relations or signs. Existing embedding methods for sign prediction generally enforce different notions of status or balance theories in their optimization function. These theories, however, are often inaccurate or incomplete, which negatively impacts method performance. In this context, we introduce conditional signed network embedding (CSNE). Our probabilistic approach models structural information about the signs in the network separately from fine-grained detail. Structural information is represented in the form of a prior, while the embedding itself is used for capturing fine-grained information. These components are then integrated in a rigorous manner. CSNE's accuracy depends on the existence of sufficiently powerful structural priors for modelling signed networks, currently unavailable in the literature. Thus, as a second main contribution, which we find to be highly valuable in its own right, we also introduce a novel approach to construct priors based on the Maximum Entropy (MaxEnt) principle. These priors can model the polarity of nodes (degree to which their links are positive) as well as signed triangle counts (a measure of the degree structural balance holds to in a network). Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt priors on their own, while less accurate than full CSNE, achieve accuracies competitive with the state-of-the-art at very limited computational cost, thus providing an excellent runtime-accuracy trade-off in resource-constrained situations

A novel regularized weighted estimation method for information diffusion prediction in social networks

Abstract In recent years, social networks have become popular among Internet users, and various studies have been performed on the analysis of users’ behavior in social networks. Information diffusion analysis is one of the leading fields in social network analysis. In this context, users are influenced by other users in the social network, such as their friends. User behavior is analyzed using several models designed for information diffusion modeling and prediction. In this paper, first, the problem of estimating the diffusion probabilities for the independent cascade model is studied. We propose a method for estimating diffusion probabilities. This method assigns a weight to each individual diffusion sample within a network. To account for the different effects of diffusion samples, several weighting schemes are proposed. Afterward, the proposed method is applied to real cascade datasets such as Twitter and Digg. We try to estimate diffusion probabilities for the independent cascade model considering the continuous time of nodes’ infections. The results of our evaluation of our methods are presented based on several datasets. The results show the high performance of our methods in terms of training time as well as other metrics such as mean absolute error and F-measure

Quantifying and reducing imbalance in networks

Real-world data can often be represented as a heterogeneous network relating nodes of different types. For example, envision a job market network, where nodes may be job seekers, skills, and jobs, and where links to skills could indicate having that skill (if linked to a job seeker) or having the skill as a requirement (for jobs). It can be relevant to consider the imbalance in such a network between the nodes of different types. In the example, this imbalance could correspond to the mismatch between supply and demand of jobs due to a mismatch in skills. Identifying and reducing such imbalance is a problem of great significance. We introduce a quantification of the imbalance in a network between two sets of nodes (nodes of different types, attributes, etc.) based on the embedding of a network, i.e., a real-valued vector space representation of the network nodes. Moreover, we introduce an algorithm named GraB (Graph Balancing) which ranks unconnected node pairs according to how well they would reduce the imbalance in a network if an edge were added between them. E.g., in the job network, GraB could be used to rank skills that job seekers do not yet have but could strive to acquire, to move them closer in the embedding towards an area where there is an abundance of jobs, and hence to reduce job market imbalance. We evaluated GraB on several datasets, including a job market network, and find that GraB outperforms baselines in reducing network imbalance

Quantifying and reducing imbalance in networks

Real-world data can often be represented as a heterogeneous network relating nodes of different types. For example, envision a job market network, where nodes may be job seekers, skills, and jobs, and where links to skills could indicate having that skill (if linked to a job seeker) or having the skill as a requirement (for jobs). It can be relevant to consider the imbalance in such a network between the nodes of different types. In the example, this imbalance could correspond to the mismatch between supply and demand of jobs due to a mismatch in skills. Identifying and reducing such imbalance is a problem of great significance. We introduce a quantification of the imbalance in a network between two sets of nodes (nodes of different types, attributes, etc.) based on the embedding of a network, i.e., a real-valued vector space representation of the network nodes. Moreover, we introduce an algorithm named GraB (Graph Balancing) which ranks unconnected node pairs according to how well they would reduce the imbalance in a network if an edge were added between them. E.g., in the job network, GraB could be used to rank skills that job seekers do not yet have but could strive to acquire, to move them closer in the embedding towards an area where there is an abundance of jobs, and hence to reduce job market imbalance. We evaluated GraB on several datasets, including a job market network, and find that GraB outperforms baselines in reducing network imbalance

A challenge-based survey of e-recruitment recommendation systems

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences. Therefore, e-recruitment recommendation systems could greatly impact job seekers' careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the companies' competitive edge in the market. Hence, the domain of e-recruitment recommendation deserves specific attention. Existing surveys on this topic tend to discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach, which we believe might be more practical to developers facing a concrete e-recruitment design task with a specific set of challenges, as well as to researchers looking for impactful research projects in this domain. We first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider promising in the e-recruitment recommendation domain