Search Results for author:
Found 8 papers, 3 papers with code
Neighbor Similarity Based Agglomerative Method for Community Detection in Networks
In the first phase, we first take out the node with the largest degree from the network to take it as an exemplar of the first community and insert its most similar neighbor node into the community as well.
IncNSA: Detecting communities incrementally from time-evolving networks based on node similarity
When the network evolves from the previous snapshot to the current one, the proposed method only considers the community affiliations of partial nodes efficiently, which are either newborn nodes or some active nodes from the previous snapshot.
The 4th International Workshop on Smart Simulation and Modelling for Complex Systems
Computer-based modelling and simulation have become useful tools to facilitate humans to understand systems in different domains, such as physics, astrophysics, chemistry, biology, economics, engineering and social science.
A Comprehensive Survey on Community Detection with Deep Learning
A community reveals the features and connections of its members that are different from those in other communities in a network.
Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks
In this paper, we construct a dual-layer graph (i. e., the news layer and the user layer) to extract multiple relations of news and users in social networks to derive rich information for detecting fake news.
Heterogeneous Social Event Detection via Hyperbolic Graph Representations
This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space.
A knowledge representation approach for construction contract knowledge modeling
The emergence of large language models (LLMs) presents an unprecedented opportunity to automate construction contract management, reducing human errors and saving significant time and costs.
Construction contract risk identification based on knowledge-augmented language model
This paper presents a novel approach that leverages LLMs with construction contract knowledge to emulate the process of contract review by human experts.
