Counting and enumeration of local topological structures, such as triangles, is an important task for analyzing large real‐life networks. For instance, triangle count in a network is used to compute transitivity—an important property for understanding graph evolution over time. Triangles are also used for various other tasks completed for real‐life networks, including community discovery, link prediction, and spam filtering. The task of triangle counting, though simple, has gained wide attention in recent years from the data mining community. This is due to the fact that most of the existing algorithms for counting triangles do not scale well to very large networks with millions (or even billions) of vertices. To circumvent this limitation, researchers proposed triangle counting methods that approximate the count or run on distributed clusters. In this paper, we discuss the existing methods of triangle counting, ranging from sequential to parallel, single‐machine to distributed, exact to approximate, and off‐line to streaming. We also present experimental results of performance comparison among a set of approximate triangle counting methods built under a unified implementation framework. Finally, we conclude with a discussion of future works in this direction. WIREs Data Mining Knowl Discov 2018, 8:e1226. doi: 10.1002/widm.1226
This article is categorized under:
Algorithmic Development > Structure Discovery
Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to acquire few additional skills which are required to assume the new position. In this work, we created three types of information networks from the historical job data: (i) job transition network, (ii) job-skill network, and (iii) skill co-occurrence network. We provide a representation learning model which can utilize the information from all three networks to jointly learn the representation of the jobs and skills in the shared k-dimensional latent space. In our experiments, we show that by jointly learning the representation for the jobs and skills, our model provides better recommendation for both jobs and skills. Additionally, we also show some case studies which validate our claims.
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