Graphframex: Towards systematic evaluation of explainability methods for graph neural networks
As one of the most popular machine learning models today, graph neural networks (GNNs)
have attracted intense interest recently, and so does their explainability. Users are …
have attracted intense interest recently, and so does their explainability. Users are …
Efficient Algorithms for Personalized PageRank Computation: A Survey
Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs.
For a pair of nodes and, the PPR value equals the probability that an-discounted random …
For a pair of nodes and, the PPR value equals the probability that an-discounted random …
Constrained social community recommendation
In online social networks, users with similar interests tend to come together, forming social
communities. Nowadays, user-defined communities become a prominent part of online …
communities. Nowadays, user-defined communities become a prominent part of online …
Approximate graph propagation
Efficient computation of node proximity queries such as transition probabilities, Personalized
PageRank, and Katz are of fundamental importance in various graph mining and learning …
PageRank, and Katz are of fundamental importance in various graph mining and learning …
Massively parallel algorithms for personalized pagerank
Personalized PageRank (PPR) has wide applications in search engines, social
recommendations, community detection, and so on. Nowadays, graphs are becoming …
recommendations, community detection, and so on. Nowadays, graphs are becoming …
Networked time series imputation via position-aware graph enhanced variational autoencoders
Multivariate time series (MTS) imputation is a widely studied problem in recent years.
Existing methods can be divided into two main groups, including (1) deep recurrent or …
Existing methods can be divided into two main groups, including (1) deep recurrent or …
Personalized pagerank on evolving graphs with an incremental index-update scheme
\em Personalized PageRank (PPR) stands as a fundamental proximity measure in graph
mining. Given an input graph G with the probability of decay α, a source node s and a target …
mining. Given an input graph G with the probability of decay α, a source node s and a target …
Reinforcement neighborhood selection for unsupervised graph anomaly detection
Unsupervised graph anomaly detection is crucial for various practical applications as it aims
to identify anomalies in a graph that exhibit rare patterns deviating significantly from the …
to identify anomalies in a graph that exhibit rare patterns deviating significantly from the …
Efficient personalized pagerank computation: A spanning forests sampling based approach
Computing the personalized PageRank vector is a fundamental problem in graph analysis.
In this paper, we propose several novel algorithms to efficiently compute the personalized …
In this paper, we propose several novel algorithms to efficiently compute the personalized …
Estimating Single-Node PageRank in Õ (min{dt, √m}) Time
PageRank is a famous measure of graph centrality that has numerous applications in
practice. The problem of computing a single node's PageRank has been the subject of …
practice. The problem of computing a single node's PageRank has been the subject of …