We study the problem of approximately counting cliques and near cliques in a graph, where the access to the graph is only available through crawling its vertices. This model has been introduced recently to capture real-life scenarios in which the entire ...

Kemeny's constant for random walks on a graph is defined as the mean hitting time from one node to another selected randomly according to the stationary distribution. It has found numerous applications and attracted considerable research interest. ...

Thebiharmonic distance (BD) is a fundamental metric that measures the distance of two nodes in a graph. It has found applications in network coherence, machine learning, and computational graphics, among others. In spite of BD's importance, efficient ...

For an undirected graph, its Kemeny's constant is defined as the mean hitting time of random walks from one vertex to another chosen randomly according to the stationary distribution. Kemeny's constant exhibits numerous explanations from different ...

A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publications, and biological interactions between drugs and proteins. k-Bipartite ...

Knowledge graph (KG) embedding techniques use relationships between entities to learn low-dimensional representations of entities and relations. The traditional KG embedding techniques (such as TransE and DistMult) estimate these embeddings using the ...

In view of the common problems in image saliency detection, such as inaccurate positioning of saliency objects and easy loss of detail information in complex scenario. This paper proposes a saliency detection based on feature fusion and weighted ...

Modern cloud native applications are generally built with a microservice architecture. To tackle various performance problems among a large number of services and machines, an end‐to‐end tracing tool is always equipped in these systems to track ...

This paper proposes TraceRank to identify root causes of performance problems with dis‐aggregated end‐to‐end traces. A dis‐aggregated trace captures the execution path and process time about how a single request traverses the system. To fully leverage the ...

Given a graph \mathcalG , the spanning centrality (SC) of an edge e measures the importance of e for \mathcalG to be connected. In practice, SC has seen extensive applications in computational biology, electrical networks, and combinatorial ...

The spread of contagions has been a central component of research on social networks. An abundance of literature shows that few nodes in the network contribute significantly to the final magnitude of the outbreak. The problem of finding the set of k ...

Hypergraphs are an omnipresent data structure used to represent high-order interactions among entities. Given a hypergraph H wherein nodes are associated with attributes, attributed hypergraph clustering (AHC) aims to partition the nodes in H into k ...

SimRank is an important metric to measure the topological similarity between two nodes in a graph. In particular, single-source and top-k SimRank has numerous applications in recommendation systems, network analysis, and web mining, etc. Mathematically, ...

Given an undirected graph G, the effective resistance r(s,t) measures the dissimilarity of node pair s,t in G, which finds numerous applications in real-world problems, such as recommender systems, combinatorial optimization, molecular chemistry, and ...

The spread of rumors in online networks threatens public safety and results in economic losses. To overcome this problem, a lot of work studies the problem of rumor control which aims at limiting the spread of rumors. However, all previous work ...$$$$$\mathrm{}\mathrm{}$$\mathrm{}\mathrm{}$

Many real-world graphs are temporal in nature, where the temporal information indicates when a particular edge is changed (e.g., edge insertion and deletion). Performing random walks on such temporal graphs is of paramount value. The state-of-the-art ...

Out-of-core random walk system has recently attracted a lot of attention as an economical way to run billions of walkers over large graphs. However, existing out-of-core random walk systems are all built upon general out-of-core graph processing ...

We study the problem of approximately counting cliques and near cliques in a graph, where the access to the graph is only available through crawling its vertices; thus typically seeing only a small portion of it. This model, known as the random walk ...

Analysis of social networks with limited data access is challenging for third parties. To address this challenge, a number of studies have developed algorithms that estimate properties of social networks via a simple random walk. However, most existing ...

With the wide use of Location-Based Social Networks (LBSNs), predicting user friendship from online social relations and offline trajectory data is of great value to improve the platform service quality and user satisfaction. Existing methods mainly focus ...

Random walk serves as a powerful tool in dealing with large-scale graphs, reducing data size while preserving structural information. Unfortunately, existing system frameworks all focus on the execution of a single walker task in serial. We propose ...