ONLINE INTELLIGENCE AND TRUST COMPUTATION IN LARGE-SCALE DYNAMIC NETWORKS
Dr Shan Xue: Department of Computing, MacquarieUniversity
Prof Jian Yang: Department of Computing, Macquarie University
Dr Jia Wu: Department of Computing, Macquarie University
IEEE World Congress on Computational Intelligence
Shenzhen,China July 18-22,2021
15 January 2021 10 February 2021
Paper Submission Deadline
15 March 2021 10 April 2021
Author notification of acceptance or rejection
30 March 2021 25 April 2021
Camera-ready Submission Deadline
18-22 July 2021
IEEE IJCNN 2021, Shenzhen, China
Note: all deadlines are 11:59 pm US pacific time.
AIM AND SCOPE
The large-scale dynamic network modelling online social life is complex in data structure, rich in knowledge and challenging in intelligent computation.
The concept of trust has long been subjective in human social activities. Measuring trust is always challenging, yet intelligent computation enhancing especially in the context of online social network (OSN). The unique applicability of modelling both temporal and spatial aspects enables dynamic networks abstracting the world in a complex data structure of graph. With the base units of a static graph, vast number of cases of combinations of nodes and edges illustrate the interactions of the real-world entities in the spatial aspect. For example, the nodes and edges represent the users and the various online social activities in a snapshot of OSN. Under the evolving graph structures, the evolutional patterns represent rich information from the temporal side. Meanwhile, the multimedia contents, e.g., natural language, images and video, denoted as the attributes of the graph entities, increase the difficulties of graph learning.
Intelligent methods have been dramatically developed in recent years. Supported by deep neural networks, graph machine learning tends to explain the dynamics either in network topologies or entity attributions. Moreover, interdisciplinary approaches of intelligent computation on trust help graph machine learning understand the complex and dynamically changing networks, target fast relevant topologies and attributes, and return more accurate predictions.
The special session aims to solicit contributions in intelligent computational frameworks, models, algorithms and applications for quantifying trust of online activities on the large-scale dynamic networks.
The topics of this session include but are not limited to
Supervised / semi-supervised / unsupervised graph machine learning
Deep neural network with input of graph structured data
Network pattern recognition
Dynamic graph embedding
Temporal attribute learning
Spatial and temporal data structure
Dynamic analysis in large-scale network
Large-scale hierarchical / heterogeneous information network
Temporal graph and graph matching within / across network(s)
Stochastic probability in dynamic graph mining
Time series learning over evolutional graph changes
Trust computation model in complex social network
Online trust learning, including trust propagation, causal reasoning, entity resolution, etc.
Graph Neural Networks
Signed Social Network Analysis
Other scenarios related to evolutional network topology and contextual attributed graph
Prepare your paper according to IJCNN 2021 policy (https://www.ijcnn.org/paper-submission-2021).
Submit your paper to this special session (https://ieee-cis.org/conferences/ijcnn2021/upload.php) by choosing the session name "S07. Online Intelligence and Trust Computation in Large-scaleDynamic Networks" as the main research topic. Please leave the additional research topic empty.
If you are a student, please tick "Student Paper (optional)" by submission for"Outstanding student paper competition of IJCNN2021".