The research field of Information Retrieval (IR) has evolved significantly, expanding beyond
traditional search to meet diverse user information needs. Recently, Large Language …

Temporal graph neural networks (T-GNNs) are state-of-the-art methods for learning
representations over dynamic graphs. Despite the superior performance, T-GNNs still suffer …

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 …

Given a graph G, a source node s and a target node t, the personalized PageRank (PPR) of t
with respect to s is the probability that a random walk starting from s terminates at t. A single …

Personalized PageRank (PPR) is a classic metric that measures the relevance of graph
nodes with respect to a source node. Given a graph G, a source node s, and a parameter k …

Personalized PageRank (PPR) has wide applications in search engines, social
recommendations, community detection, and so on. Nowadays, graphs are becoming …

Personalized PageRank (PPR) is a critical measure of the importance of a node t to a source
node s in a graph. The Single-Source PPR (SSPPR) query computes the PPR's of all the …

Personalized PageRank (PPR) is a widely used node proximity measure in graph mining
and network analysis. Given a source node s and a target node t, the PPR value π (s, t) …

Personalized PageRank (PPR) computation is a fundamental operation in web search,
social networks, and graph analysis. Given a graph G, a source s, and a target t, the PPR …

\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 …