82 citations · 195 across the 17 of their papers we have counts for
20 papers
FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism
Peng Fang, Arijit Khan, Ziqiang Wu +4
Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and graph-based retrieval-augmented generation (Graph…
Knowledge Graphs Querying
Arijit Khan
Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples -- that c…
Distributed Graph Embedding with Information-Oriented Random Walks
Peng Fang, Arijit Khan, Siqiang Luo +5
Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the imp…
Data Depth and Core-based Trend Detection on Blockchain Transaction Networks
Jason Zhu, Arijit Khan, Cuneyt Gurcan Akcora
Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to th…
Neighborhood-based Hypergraph Core Decomposition
Naheed Anjum Arafat, Arijit Khan, Arpit Kumar Rai +1
We propose neighborhood-based core decomposition: a novel way of decomposing hypergraphs into hierarchical neighborhood-cohesive subhypergraphs. Alternative approaches to decomposi…
Most Probable Densest Subgraphs
Arkaprava Saha, Xiangyu Ke, Arijit Khan +1
Computing the densest subgraph is a primitive graph operation with critical applications in detecting communities, events, and anomalies in biological, social, Web, and financial n…
Voting-based Opinion Maximization
Arkaprava Saha, Xiangyu Ke, Arijit Khan +1
We investigate the novel problem of voting-based opinion maximization in a social network: Find a given number of seed nodes for a target campaigner, in the presence of other compe…
Aggregate Queries on Knowledge Graphs: Fast Approximation with Semantic-aware Sampling
Yuxiang Wang, Arijit Khan, Xiaoliang Xu +3
A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a schema-flexible manner. Aggregate query is a fundamental query over KGs, e.g., "what is the aver…
Multi-relation Graph Summarization
Xiangyu Ke, Arijit Khan, Francesco Bonchi
Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk s…
Maximizing Contrasting Opinions in Signed Social Networks
Kaivalya Rawal, Arijit Khan
The classic influence maximization problem finds a limited number of influential seed users in a social network such that the expected number of influenced users in the network, fo…
Efficiently Embedding Dynamic Knowledge Graphs
Tianxing Wu, Arijit Khan, Melvin Yong +2
Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answe…
Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs
Yuxiang Wang, Arijit Khan, Tianxing Wu +2
Recently, graph query is widely adopted for querying knowledge graphs. Given a query graph , the graph query finds subgraphs in a knowledge graph that exactly or approxima…
Distance-generalized Core Decomposition
Francesco Bonchi, Arijit Khan, Lorenzo Severini
The -core of a graph is defined as the maximal subgraph in which every vertex is connected to at least other vertices within that subgraph. In this work we introduce a dista…
An In-Depth Comparison of s-t Reliability Algorithms over Uncertain Graphs
Xiangyu Ke, Arijit Khan, Leroy Lim Hong Quan
Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging applications, and have recently attracted the attention of the datab…
Reliability Maximization in Uncertain Graphs
Xiangyu Ke, Arijit Khan, Mohammad Al Hasan +1
Network reliability measures the probability that a target node is reachable from a source node in an uncertain graph, i.e., a graph where every edge is associated with a probabili…
Composite Hashing for Data Stream Sketches
Arijit Khan, Sixing Yan
In rapid and massive data streams, it is often not possible to estimate the frequency of items with complete accuracy. To perform the operation in a reasonable amount of space and…
Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach
Vijaya Krishna Yalavarthi, Arijit Khan
We propose a generalized framework for influence maximization in large-scale, time evolving networks. Many real-life influence graphs such as social networks, telephone networks, a…
Select Your Questions Wisely: For Entity Resolution With Crowd Errors
Vijaya Krishna Yalavarthi, Xiangyu Ke, Arijit Khan
Crowdsourcing is becoming increasingly important in entity resolution tasks due to their inherent complexity such as clustering of images and natural language processing. Humans ca…
Conditional Reliability in Uncertain Graphs
Arijit Khan, Francesco Bonchi, Francesco Gullo +1
Network reliability is a well-studied problem that requires to measure the probability that a target node is reachable from a source node in a probabilistic (or uncertain) graph, i…
Querying Knowledge Graphs by Example Entity Tuples
Nandish Jayaram, Arijit Khan, Chengkai Li +2
We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content ri…