Modern Big Data increasingly appears in the form of complex networks and graphs. Examples include social networks, citation networks, communication networks, the World Wide Web. Researchers make use of network-based solutions for solving problems for diverse disciplines, including social mining, transportation, bioinformatics, computational science, health care and intelligence analysis. However, the massive sizes, multiple types of entities (users, documents, items etc.), user behaviours and relations between entities that nowadays characterise most networks, have increased the challenge of methodologies that analyse and mine complex networks. To address these challenges, machine learning models are often used for analysing and mining large-scale networks. Furthermore, machine learning techniques enable novel methods of describing generative models for networks structures, dynamics and communities.
The workshop will be co-located with the International Joint Conference on Neural Networks IJCNN 2017. The workshop intends to facilitate the exchange of ideas between different research communities from both academia and industry, working at the intersection of machine learning and (social/complex) networks. The workshop focus will encompass machine learning algorithms for building and analysing large-scale networks, such as social networks, citation networks, etc. The workshop will host two keynote speakers (one from academia and one from industry), which will be announced at a later date.
Call for paper
Draft paper submission deadline：2017-03-15
Topics of submission
We are soliciting novel and original research contributions related to machine learning-based approaches to building, analysing and mining complex networks. In particular, topics of interest include but are not limited to:
Machine learning approaches to building and mining social networks
Clustering and ranking methods for big networks
Large-scale link prediction algorithms
User influence analysis
Community detection in large-scale networks
Machine learning applications and challenges in mining big networks
Distributed deep learning
Deep learning with neural networks and TensorFlow