@rijitK

Data management for emerging problems in large graphs. IEEE Senior Member, ACM Distinguished Speaker, Associate Professor @ Bowling Green State University, USA.

 USA
cs.bgsu.edu/arijitk/
Joined May 2012
  • Tweets2.4K
  • Following516
  • Followers380
62Media
Our #PAKDD2026 tutorial slides "LLMs+Graphs". llms-graphs.github.io/ We discuss 1. LLMs for Graphs 2. Graphs for LLMs 3. Knowledge Graphs for LLMs 4. LLMs for Knowledge Graphs 5. Graphs for AI Agents & 6. AI Agents for Graphs, bringing out the full synergy of LLM+Graph systems.

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

PAKDD 2026 Tutorial

llms-graphs.github.io
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Excited to demo Declarative Explanations for GNNs at #ICDM2026! - "Declarative Explanations for Graph Neural Networks: A Demonstration" arxiv.org/abs/2610.26104 We’re introducing #DeclarativeExplanation, a fresh way to make #GNNs interpretable.

Declarative Explanations for Graph Neural Networks: A Demonstration

Graph Neural Networks (GNNs) have proven effective for graph analytical tasks, yet their "black-box" nature complicates the understanding of their layer-wise behavior and hinders their application...

arxiv.org
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Glad to share our recently accepted #SIGMOD2027 paper. In this work, we introduce a cost-efficient RAG for entity matching that reduces computational overhead through blocking-based batch processing for retrieval and generation. #EntityMatching #GraphRAG arxiv.org/abs/2602.05708
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Grateful to share my reminiscence on "Mining Frequent Patterns without Candidate Generation" by Jiawei Han, Jian Pei, and Yiwen Yin in the September 2026 #SIGMODRecord Reminiscences column. sigmodrecord.org/publication…
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#ICDM2026 Demo paper accepted. "Declarative Explanations for Graph Neural Networks: A Demonstration" by Cibo Yu, Haitong Tang, Yinghui Wu, Arijit Khan, Tingting Zhu, Tingyang Chen, and Xiangyu Ke. Watch the video youtube.com/watch?v=g6u3_dwD…, and stay tuned for the camera ready!
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our #SIGMOD2027 paper studies the role of KG-RAG (knowledge graph based retrieval-augmented generation) in domain-specific entity matching. Preprint: arxiv.org/abs/2602.05708. Lead by @DylanMa2018 (my postdoc) and Zeyu Zhang, in collaboration with Sebastian Schelter and @pgroth

Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based...

Large language models (LLMs) offer a compelling approach to entity matching (EM) in label-scarce settings, yet their matching quality is sensitive to records with ambiguous or sparse attributes...

arxiv.org
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Camera-ready of our @VLDBconf #VLDB26 #VLDB2026 paper: Peng Fang, Arijit Khan, Ziqiang Wu, Zhenli Li, Yibo Zhou, Fang Wang, and Dan Feng, "FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism" vldb.org/pvldb/vol19/p2803-f… github.com/RocmFang/FeLoG
Arijit K@rijitKJun 24
Sharing our PVLDB 2026 paper "FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism" arxiv.org/abs/2606.22180 #GraphEmbedding #GNNs #FeedbackLoop #GraphDataManagement #GraphML #PVLDB26 #PVLDB2026 #VLDB26 #VLDB2026 @VLDBconf

FeLoG: Scalable and Efficient Distributed Graph Embedding with...

Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and graph-based retrieval-augmented generation (GraphRAG). As graphs...

arxiv.org
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Camera-ready of our #VLDB2026 #VLDB26 tutorial paper: S. Bhowmick, A. Khan, and B. Choi, "From Human-Graph to Agent-Graph Interaction: State of the Art and Future Directions". vldb.org/pvldb/vol19/p4878-b…
Arijit K@rijitKJun 10
3. [Tutorial] S. S Bhowmick, B. Choi, A. Khan, "From Human-Graph to Agent-Graph Interaction: State of the Art and Future Directions" #PVLDB #VLDB2026 #VLDB26 @pvldb 3/n
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Camera-ready of #VLDB2026 demo: H. Wang, H. Tang, J. Fu, A. Khan, S. Mehrotra, X. Ke, and Y. Gao. LLM-CER: An Interactive System for In-context Clustering-based Entity Resolution with Large Language Models vldb.org/pvldb/vol19/p4746-k…, youtube.com/watch?v=vDhntqB2…, github.com/AAWHY/LLMCER
Arijit K@rijitKJun 10
2. [Demonstration paper] H. Wang, H. Tang, J. Fu, A. Khan, S. Mehrotra, X. Ke, and Y. Gao, "LLM-CER: An Interactive System for In-context Clustering-based Entity Resolution with Large Language Models". 2/n
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The preprint and code of our #CIKM2026 #CIKM26 paper are here - arxiv.org/abs/2608.27153 github.com/FrappaN/ccgnn "Inductive Correlation Clustering with Graph Neural Networks" - Francesco Paolo Nerini, @FrancescoBonchi, Arijit Khan, André Panisson 👇

Inductive Correlation Clustering with Graph Neural Networks

Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specified number of...

arxiv.org
Arijit K@rijitKAug 11
"Inductive Correlation Clustering with Graph Neural Networks" by F. P. Nerini, @FrancescoBonchi, A. Khan, & A. Panisson accepted as full research paper @ #CIKM2026 #CIKM26. Stay tuned for camera ready #GraphML #CorrelationClustering #GNN for #CombinatorialOptimizationProblem
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Wonderful news to get our paper accepted at #EMNLP2026 #EMNLP26. Yuanming Zhang, Yan Lin, Arijit Khan, and Huaiyu Wan, "Large Language Model Prompt Datasets: An In-depth Analysis and Insights", EMNLP (Findings). Preprint - arxiv.org/abs/2510.09316 Stay tuned for the camera ready.

A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns,...

We compile 129 public LLM prompt datasets with more than 1.22TB and more than 673M instances and organize them into a unified taxonomy. We use seven datasets for detailed analysis and identify...

arxiv.org
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Arijit K retweeted
Yunyao Li
@yunyao_liAug 15
Looking forward to the workshop!
Arijit K@rijitKAug 15
Exciting keynotes by Prof. Chen Li (UC Irvine), @yunyao_li (Adobe), Eric Lo (CUHK), and Cheng Chen (ByteDance). 👇#AgentsGraph #VLDB26 #VLDB2026 @VLDBconf
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Exciting keynotes by Prof. Chen Li (UC Irvine), @yunyao_li (Adobe), Eric Lo (CUHK), and Cheng Chen (ByteDance). 👇#AgentsGraph #VLDB26 #VLDB2026 @VLDBconf
Arijit K@rijitKAug 15
Excited to share details for the Agents+Graph Workshop @VLDBconf #VLDB2026, to be held on September 4, 2026, in Boston, MA. seucoin.github.io/workshop/a… seucoin.github.io/workshop/a… Agents+Graph explores how AI agents and graph data can jointly redefine the future of data-driven systems.
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Excited to share details for the Agents+Graph Workshop @VLDBconf #VLDB2026, to be held on September 4, 2026, in Boston, MA. seucoin.github.io/workshop/a… seucoin.github.io/workshop/a… Agents+Graph explores how AI agents and graph data can jointly redefine the future of data-driven systems.
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sharing our 2 paper acceptance 1) Fu, Tang, Khan, Mehrotra, Ke, Gao, "LLM-Powered Clustering for Entity Resolution", #VLDBJournal 2) Amer-Yahia, Khan, Lehner, Mehrotra, Zhang, "Managing Vector Data for Retrieval Augmented Generation@Dagstuhl Seminar Summary", ACM #SIGMODRecord
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"Inductive Correlation Clustering with Graph Neural Networks" by F. P. Nerini, @FrancescoBonchi, A. Khan, & A. Panisson accepted as full research paper @ #CIKM2026 #CIKM26. Stay tuned for camera ready #GraphML #CorrelationClustering #GNN for #CombinatorialOptimizationProblem
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I am incredibly proud to lead this work. In this work, we outline a vision for trustworthy and cost-efficient data integration with #AgenticRAG. Huge thanks to you for your guidance and support. @rijitK
Arijit K@rijitKJul 27
Congratulations to my postdoc Chuangtao Ma @DylanMa2018 at Aalborg University for leading this effort!
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Our Tutorial "LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems" is featured in one of the recent episodes of 'Learning GenAI' podcast, generated by Google NotebookLM. Listen it in Spotify: podcasters.spotify.com/pod/s…
Arijit K@rijitKJun 10
Our #PAKDD2026 tutorial slides "LLMs+Graphs". llms-graphs.github.io/ We discuss 1. LLMs for Graphs 2. Graphs for LLMs 3. Knowledge Graphs for LLMs 4. LLMs for Knowledge Graphs 5. Graphs for AI Agents & 6. AI Agents for Graphs, bringing out the full synergy of LLM+Graph systems.

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

PAKDD 2026 Tutorial

llms-graphs.github.io
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I'm serving as DASFAA'27 Tutorial Track Program Co-chair, w/ Guoliang Li & Alex Zhou. The 32nd Intl. Conf. on Database Systems for Advanced Applications (DASFAA) will be held on May 28–30, 2027 at Shenyang, China. An early call for DASFAA'27 tutorials. dasfaa2027.github.io/orgcmte…
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Congratulations to my postdoc Chuangtao Ma @DylanMa2018 at Aalborg University for leading this effort!
Arijit K@rijitKJul 27
Sharing the preprint of our accepted #IEEE #TCDE #Bulletin paper "Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG" arxiv.org/abs/2607.22319 - with @DylanMa2018 based on our recent works on #RAG and #KG_RAG based #data_integration.

Towards Trustworthy and Cost-Efficient Data Integration: From...

Large language models (LLMs) and AI agents have demonstrated strong potential for data integration in zero-shot and few-shot settings. However, they continue to face significant accuracy and cost...

arxiv.org
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Joint work w/ - Amalie K Andersen, Hadi Mehdizavareh, Arijit Khan, Tobias Becher, Simone Britsch, Markward Britsch, Morten Bottcher, Simon Winther, Palle D Rohde, Morten H Jensen, and Simon L Cichosz. Congratulations to my PhD student Hadi Mehdizavareh at Aalborg University!
Arijit K@rijitKJul 27
Sharing the preprint of our accepted paper at Journal of the American Medical Informatics Association'26. "Tipping the Balance: Impact of Class Imbalance Correction on the Performance of Clinical Risk Prediction Models" - arxiv.org/abs/2603.00208

Tipping the Balance: Impact of Class Imbalance Correction on the...

Objective: ML-based clinical risk prediction models are increasingly used to support decision-making in healthcare. While class-imbalance correction techniques are commonly applied to improve...

arxiv.org
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