236 citations · 286 across the 15 of their papers we have counts for
15 papers
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation
Edoardo D'Amico, Aonghus Lawlor, Neil Hurley
The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it h…
Item Graph Convolution Collaborative Filtering for Inductive Recommendations
Edoardo D'Amico, Khalil Muhammad, Elias Tragos +3
Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the ed…
Posterior Regularization on Bayesian Hierarchical Mixture Clustering
Weipeng Huang, Tin Lok James Ng, Nishma Laitonjam +1
Bayesian hierarchical mixture clustering (BHMC) improves traditionalBayesian hierarchical clustering by replacing conventional Gaussian-to-Gaussian kernels with a Hierarchical Diri…
Partially Observable Markov Decision Process Modelling for Assessing Hierarchies
Weipeng Huang, Guangyuan Piao, Raul Moreno +1
Hierarchical clustering has been shown to be valuable in many scenarios. Despite its usefulness to many situations, there is no agreed methodology on how to properly evaluate the h…
Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach
Weipeng Huang, Nishma Laitonjam, Guangyuan Piao +1
This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset. In such a clustering, each data item is associated with exactly one leaf node and each in…
Story Disambiguation: Tracking Evolving News Stories across News and Social Streams
Bichen Shi, Thanh-Binh Le, Neil Hurley +1
Following a particular news story online is an important but difficult task, as the relevant information is often scattered across different domains/sources (e.g., news articles, b…
Be In The Know: Connecting News Articles to Relevant Twitter Conversations
Bichen Shi, Georgiana Ifrim, Neil Hurley
In the era of data-driven journalism, data analytics can deliver tools to support journalists in connecting to new and developing news stories, e.g., as echoed in micro-blogs such…
Model-based clustering in networks with Stochastic Community Finding
Aaron F. McDaid, Brendan Thomas Murphy, Nial Friel +1
In the model-based clustering of networks, blockmodelling may be used to identify roles in the network. We identify a special case of the Stochastic Block Model (SBM) where we cons…
Percolation Computation in Complex Networks
Fergal Reid, Aaron McDaid, Neil Hurley
K-clique percolation is an overlapping community finding algorithm which extracts particular structures, comprised of overlapping cliques, from complex networks. While it is concep…
Clustering in networks with the collapsed Stochastic Block Model
Aaron F. McDaid, Thomas Brendan Murphy, Nial Friel +1
An efficient MCMC algorithm is presented to cluster the nodes of a network such that nodes with similar role in the network are clustered together. This is known as block-modelling…
Normalized Mutual Information to evaluate overlapping community finding algorithms
Aaron F. McDaid, Derek Greene, Neil Hurley
Given the increasing popularity of algorithms for overlapping clustering, in particular in social network analysis, quantitative measures are needed to measure the accuracy of a me…
Diffusion in Networks With Overlapping Community Structure
Fergal Reid, Neil Hurley
In this work we study diffusion in networks with community structure. We first replicate and extend work on networks with non-overlapping community structure. We then study diffusi…
Partitioning Breaks Communities
Fergal Reid, Aaron McDaid, Neil Hurley
Considering a clique as a conservative definition of community structure, we examine how graph partitioning algorithms interact with cliques. Many popular community-finding algorit…
Seeding for pervasively overlapping communities
Conrad Lee, Fergal Reid, Aaron McDaid +1
In some social and biological networks, the majority of nodes belong to multiple communities. It has recently been shown that a number of the algorithms that are designed to detect…
Using Model-based Overlapping Seed Expansion to detect highly overlapping community structure
Aaron F. McDaid, Neil J. Hurley
As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been pro…