Community Mining from Multi-relational Networks

Neural Networks in Multi-Relational Data Mining

Neural networks are non-parametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Multi-relational data mining framework is based on the search for interesting patterns in the relational database. Multi-relational data mining algorithms search a large hypothesis space in order to find a suitable model for a given data set. Although neural networks ...

Framework of Regression-Based Graph Matrix Analysis Approach in Multi-Relational Social Network Problem

Community mining is one of the major directions in social network analysis. Social network analysis has attracted much attention in recent years. Most of the existing methods on community mining assume that there is only one kind of relation in the network and moreover, the mining results are independent of the users’ needs or preferences. However, in reality, there exist multiple, heterogeneou...

Running Multi-relational Data Mining Processes in the Cloud: A Practical Approach for Social Networks

Learning Multi-Relational Semantics Using Neural-Embedding Models

Real-world entities (e.g., people and places) are often connected via relations, forming multirelational data. Modeling multi-relational data is important in many research areas, from natural language processing to biological data mining [6]. Prior work on multi-relational learning can be categorized into three categories: (1) statistical relational learning (SRL) [10], such as Markovlogic netw...

Logical and Relational Learning

I use the term logical and relational learning (LRL) to refer to the subfield of machine learning and data mining that is concerned with learning in expressive logical or relational representations. It is the union of inductive logic programming, (statistical) relational learning and multi-relational data mining and constitutes a general class of techniques and methodology for learning from str...