JobMiner: a real-time system for mining job-related patterns from social media

The various kinds of booming social media not only provide a platform where people can communicate with each other, but also spread useful domain information, such as career and job market information. For example, LinkedIn publishes a large amount of messages either about people who want to seek jobs or companies who want to recruit new members. By collecting information, we can have a better understanding of the job market and provide insights to job-seekers, companies and even decision makers. In this paper, we analyze the job information from the social network point of view. We first collect the job-related information from various social media sources. Then we construct an inter-company job-hopping network, with the vertices denoting companies and the edges denoting flow of personnel between companies. We subsequently employ graphmining techniques to mine influential companies and related company groups based on the job-hopping network model. Demonstration on LinkedIn data shows that our system JobMiner can provide a better understanding of the dynamic processes and a more accurate identification of important entities in the job market.

[1]  Ross J. Anderson,et al.  Temporal node centrality in complex networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

[3]  Adilson E Motter,et al.  Network observability transitions. , 2012, Physical review letters.

[4]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Alok N. Choudhary,et al.  CluChunk: clustering large scale user-generated content incorporating chunklet information , 2012, BigMine '12.

[7]  Yihong Gong,et al.  Detecting communities and their evolutions in dynamic social networks—a Bayesian approach , 2011, Machine Learning.

[8]  Nagiza F. Samatova,et al.  Community-based anomaly detection in evolutionary networks , 2012, Journal of Intelligent Information Systems.

[9]  Jiawei Han,et al.  Mining topic-level influence in heterogeneous networks , 2010, CIKM.

[10]  Wei-keng Liao,et al.  Learning to Group Web Text Incorporating Prior Information , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.