Collaboration between researchers is recognized as being important in modern academia. The reason of is that it can offer lots of benefits for both academic research as well as scientific researches. In the sense that, how to exploit valuable information from collaboration network is becoming more and more attractive and meaningful.
Our research mainly focuses on predicting and discovering the patterns and influencing factors of academic success, generally the science of success in science. The most anticipated goal of our research includes: (1) Discovering the success spectrum of scholarly entities, in other words, building the academic DNA, (2) Constructing a comprehensive and scientific evaluation system, and (3) Predicting the future success.
So exciting, in the above mentioned filed, we have finished several achievements, such as (1) conference closure based on the triadic closure and focal closure (JCDL’16, Can Academic Conferences Promote Research Collaboration?). (2) deep learning model to infer advisor-advisee relationships based on dynamic scientific collaboration networks (JCDL’16, Mining Advisor-Advisee Relationships in Scholarly Big Data: A Deep Learning Approach).(3) exploring whether the sustainability of scientific collaboration including collaboration duration and collaboration times can be predicted(IW3C2’17, Is Scientific Collaboration Sustainability Predictable?).
The large, complex networks can be considered as structures whose nodes represent entities embedded in different context, and edges represent interactions between entities. Exploring the inner structure and identifying the mechanisms by which interactions evolve is a fundamental question that is still not well-understood. Higher order organizations in networks, i.e., motifs, have recently drawn a lot of attention.
Our research mainly focuses on mining, analyzing, and predicting interactions in multiple networks (e.g., academic networks, social network, and protein networks). We try to seek answers for following challenging questions from Big Network perspective: (1) How to mining the implicit interactions with limited information in the dynamic networks? (2) How to efficiently analyze the networks in higher order structures from novel viewpoints? (3) How to understand, profile, and analyze motifs in networks?
We develop approaches to infer advisor-advisee relationships based on dynamic scientific collaboration networks(WWW’17, Shifu: Deep Learning Based Advisor-advisee Relationship Mining in Scholarly Big Data). We are making efforts in handling especially high computational complexity brought by large-scale networks. Moreover, we aiming at proposing universal methods to solve network-based issues.
Knowledge is a familiarity, awareness, or understanding of someone or something, such as facts, information, descriptions, or skills. The evolutionary history of knowledge reflects the process of human to understand the world. Recently, data science revolutionizes everything, providing us the unprecedented ability to uncover the law between knowledge and human behavior.
Against this general background, we mainly focus on knowledge mining from two angles: education and knowledge graph. On one hand, education, as the specialized activity to deliver knowledge, contains tremendous knowledge-related information. We collect education data from various sources and explore student behavior from three meaningful aspects: academic performance, psychological condition and career development. On the other hand, as a crux graph-based description of the entities in the real world, knowledge graph raises a surge attention in the fields of network science and data science. Three directions we devoted in knowledge graph include the construction, representation and inference.
Matching refers to find the formation of mutually beneficial relationships among entities. In the real world, there are a large number of problems modeled as matching problems such as person-organization fit (two-side matching), retrieval (semantic matching), recommendation (item-user matching), classification and clustering (feature-label matching) and so on.
Matching group aims at discovering matching mechanisms hiding in the real-world problems, meanwhile, the group devotes itself to seeking corresponding matching methods and applications under the age of big data. Several core challenges, such as the tools and algorithms for solving matching problems which are dramatically complex for data mining, will be the main directions we will focus on. In matching group, we will explore the most promising areas of research in matching theory to find novel scalable matching techniques and frameworks capable of being applied in practice. The topic we concern about is inherently interdisciplinary and spans aspects such as network science, data science and artificial intelligence.
- * Big Data
- * Computational Social Science
- * Social Computing
- * Mobile Computing