Search Results for author:
Found 8 papers, 4 papers with code
Multi Agent Path Finding with Awareness for Spatially Extended Agents
XCBS-A generates a huge search space impacting its efficiency in terms of memory; to address this we propose an approach for memory-efficiency and empirically demonstrate the performance of the algorithm.
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description
Real world networks often come with (community) outlier nodes, which behave differently from the regular nodes of the community.
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks
To the best of our knowledge, this is the first direct unsupervised approach for edge embedding in homogeneous information networks, without relying on the node embeddings.
Subgraph Attention for Node Classification and Hierarchical Graph Pooling
Along with attention over the subgraphs, our pooling architecture also uses attention to determine the important nodes within a level graph and attention to determine the important levels in the whole hierarchy.
Neural Cross-Domain Collaborative Filtering with Shared Entities
Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains.
Outlier Aware Network Embedding for Attributed Networks
We also consider different downstream machine learning applications on networks to show the efficiency of ONE as a generic network embedding technique.
FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
In this work, we propose a nonnegative matrix factorization based optimization framework, namely FSCNMF which considers both the network structure and the content of the nodes while learning a lower dimensional vector representation of each node in the network.
Social and Information Networks
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.
