{"type":"ScholarlyArticle","title":"Efficient Correlated Subgraph Searches for AI-powered Drug Discovery","authors":[{"name":"Hiroaki Shiokawa","slug":"hiroaki-shiokawa","given":"Hiroaki","family":"Shiokawa","affiliation":"Center for Computational Sciences , University of Tsukuba , Japan"},{"name":"Yuma Naoi","slug":"yuma-naoi","given":"Yuma","family":"Naoi","affiliation":"Graduate School of Science and Technology , University of Tsukuba , Japan"},{"name":"Shohei Matsugu","slug":"shohei-matsugu","given":"Shohei","family":"Matsugu","affiliation":"Graduate School of Science and Technology , University of Tsukuba , Japan"}],"abstract":"Correlated subgraph searches (CSSs) are essential building blocks for AI-powered drug discovery. Given a query molecule modeled as a graph, CSS finds top-k molecules correlated to the query in a database. However, the cost increases exponentially with the molecule size. Herein we present Corgi, a framework to accelerate CSS methods while ensuring top-k search accuracy. Corgi dynamically excludes unnecessary subgraphs to overcome the expensive cost without sacrificing search accuracy. Our experimental analysis confirms that Corgi has a shorter running time and improved accuracy compared to existing state-of-the-art methods, while a case study demonstrates that Corgi is suitable for practical AI-powered drug discovery.","doi":"10.24963/ijcai.2024/260","url":"https://ijcai.org/proceedings/2024/260","pdf":"https://ijcai.org/proceedings/2024/0260.pdf","markdown":"https://ijcai.org/proceedings/2024/260.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/260","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":260,"pages":{"first":2351,"last":2361},"keywords":["Data Mining: DM: Applications","Data Mining: DM: Mining graphs","Multidisciplinary Topics and Applications: MTA: Other","Data Mining: DM: Big data and scalability"],"videos":[],"references":[{"position":1,"raw":"[Abdel-Hafiz et al., 2022] Mohamed Abdel-Hafiz, Mesbah Najafi, Shahab Helmi, Katherine A Pratte, Yonghua Zhuang, Weixuan Liu, Katerina J Kechris, Russell P Bowler, Leslie Lange, and Farnoush Banaei-Kashani. Sig- nificant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification. Frontiers in Big Data, 5:20, 2022.","title":"Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification","authors":"Mohamed Abdel-Hafiz, Mesbah Najafi, Shahab Helmi, Katherine A Pratte, Yonghua Zhuang, Weixuan Liu, Katerina J Kechris, Russell P Bowler, Leslie Lange, Farnoush Banaei-Kashani","year":"2022","venue":"Frontiers in Big Data","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Askr et al., 2023] Heba Askr, Enas Elgeldawi, Heba Aboul Ella, Yaseen A. M. M. Elshaier, Mamdouh M. Gomaa, and Aboul Ella Hassanien. Deep Learning in Drug Discovery: An Integrative Review and Future Challenges. Artificial Intelligence Review, 56(7):5975-6037, 2023.","title":"Deep Learning in Drug Discovery: An Integrative Review and Future Challenges","authors":"Heba Askr, Enas Elgeldawi, Heba Aboul Ella, A M M Yaseen, Elshaier, M Mamdouh, Aboul Gomaa, Ella Hassanien","year":"2023","venue":"Artificial Intelligence Review","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bhattacharjee and Vlachos, 2020] Himaghna Bhattacharjee and Dionisios G. Vlachos. Thermochemical Data Fusion Using Graph Representation Learning. Journal of Chemi- cal Information and Modeling, 60:4673-4683, 2020.","title":"Thermochemical Data Fusion Using Graph Representation Learning","authors":"Vlachos Bhattacharjee, Himaghna Bhattacharjee, Dionisios G Vlachos","year":"2020","venue":"Journal of Chemical Information and Modeling","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chowdhury et al., 2021] Mohammad Ehsan Shahmi Chowdhury, Chowdhury Farhan Ahmed, and Carson K. Leung. A New Approach for Mining Correlated Fre- quent Subgraphs. ACM Transactions on Management Information Systems, 13(1):28, 2021.","title":"A New Approach for Mining Correlated Frequent Subgraphs","authors":"Mohammad Ehsan, Shahmi Chowdhury, Chowdhury Farhan Ahmed, Carson K Leung","year":"2021","venue":"ACM Transactions on Management Information Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Demir et al., 2022] Andac Demir, Baris Coskunuzer, Yu- lia R. Gel, Ignacio Segovia-Dominguez, Yuzhou Chen, and Bulent Kiziltan. ToDD: Topological Compound Fin- gerprinting in Computer-Aided Drug Discovery. In Pro- ceedings of Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS2022, 2022.","title":"ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery","authors":"Demir","year":"2022","venue":"Proceedings of Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Elseidy et al., 2014] Mohammed Elseidy, Ehab Abdel- hamid, Spiros Skiadopoulos, and Panos Kalnis. Grami: Frequent subgraph and pattern mining in a single large graph. Proceedings of VLDB Endowment, 7(7):517-528, 2014.","title":"Grami: Frequent subgraph and pattern mining in a single large graph","authors":"Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, Panos Kalnis","year":"2014","venue":"Proceedings of VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Garcia-Hernandez et al., 2019] Carlos Garcia-Hernandez, Alberto Fernández, and Francesc Serratosa. Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure. Journal of Chemical Information and Modeling, 59:1410-1421, 2019.","title":"Carlos Garcia-Hernandez, Alberto Fernández, and Francesc Serratosa. Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure","authors":"Garcia-Hernandez","year":"2019","venue":"Journal of Chemical Information and Modeling","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Goyal et al., 2020] Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu. GraphGen: A Scalable Approach to Domain- agnostic Labeled Graph Generation. In Proceedings of The Web Conference 2020, WWW2020, page 1253-1263, 2020.","title":"GraphGen: A Scalable Approach to Domainagnostic Labeled Graph Generation","authors":"Nikhil Goyal, Harsh Vardhan Jain, Sayan Ranu","year":"2020","venue":"Proceedings of The Web Conference 2020","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Irwin et al., 2012] John J. Irwin, Teague Sterling, Michael M. Mysinger, Erin S. Bolstad, and Ryan G. Coleman. ZINC: A Free Tool to Discover Chemistry for Biology. Journal of Chemical Information and Modeling, 52(7):1757--1768, 2012.","title":"ZINC: A Free Tool to Discover Chemistry for Biology","authors":"John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, Ryan G Coleman","year":"2012","venue":"Journal of Chemical Information and Modeling","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kawano et al., 2024] Keisuke Kawano, Satoshi Koide, Hi- roaki Shiokawa, and Toshiyuki Amagasa. Multi- Dimensional Fused Gromov Wasserstein Discrepancy for Edge-Attributed Graphs. IEICE Transactions on Informa- tion and Systems, E107.D(5):683-693, 2024.","title":"Multi-Dimensional Fused Gromov Wasserstein Discrepancy for Edge-Attributed Graphs","authors":"Keisuke Kawano, Satoshi Koide, Hiroaki Shiokawa, Toshiyuki Amagasa","year":"2024","venue":"IEICE Transactions on Information and Systems, E","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ke et al., 2007] Yiping Ke, James Cheng, and Wilfred Ng. Correlation Search in Graph Databases. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD2007, page 390-399, 2007.","title":"Correlation Search in Graph Databases","authors":"Yiping Ke, James Cheng, Wilfred Ng","year":"2007","venue":"Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD2007","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ke et al., 2008] Yiping Ke, James Cheng, and Wilfred Ng. Efficient Correlation Search from Graph Databases. IEEE Transactions on Knowledge and Data Engineering, 20(12):1601-1615, 2008.","title":"Efficient Correlation Search from Graph Databases","authors":"Yiping Ke, James Cheng, Wilfred Ng","year":"2008","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ke et al., 2009] Yiping Ke, James Cheng, and Jeffrey Xu Yu. Top-k Correlative Graph Mining. In Proceedings of the SIAM International Conference on Data Mining, SDM2009, pages 1038-1049, 2009.","title":"Top-k Correlative Graph Mining","authors":"Yiping Ke, James Cheng, Jeffrey Xu, Yu","year":"2009","venue":"Proceedings of the SIAM International Conference on Data Mining, SDM2009","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Latsiou and Papadopoulos, 2011] Georgia S. Latsiou and Apostolos N. Papadopoulos. Incremental Discovery of Top-k Correlative Subgraphs. In Proceedings of the 15th Panhellenic Conference on Informatics, PCI2011, pages 56-60, 2011.","title":"Incremental Discovery of Top-k Correlative Subgraphs","authors":"Georgia S Latsiou, Apostolos N Papadopoulos","year":"2011","venue":"Proceedings of the 15th Panhellenic Conference on Informatics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lee et al., 2014] En-Shiun Annie Lee, Sanderz Fung, Ho- Yin Sze-To, and Andrew K C Wong. Discovering Co- occurring Patterns and Their Biological Significance in Protein Families. BMC Bioinformatics, 15(S2):13, 2014.","title":"Discovering Cooccurring Patterns and Their Biological Significance in Protein Families","authors":"Lee","year":"2014","venue":"BMC Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lin et al., 2020] Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, and Xiangxiang Zeng. KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Predic- tion. In Proceedings of the 29th International Joint Con- ference on Artificial Intelligence, IJCAI 2020, pages 2739- 2745, 7 2020.","title":"KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction","authors":"Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, Xiangxiang Zeng","year":"2020","venue":"Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020","doi":null,"url":null,"internal_url":"/proceedings/2020/380"},{"position":17,"raw":"[Lipinski and Hopkins, 2004] Christopher Lipinski and An- drew Hopkins. Navigating Chemical Space for Biology and Medicine. Nature, 432:855-861, 2004.","title":"Navigating Chemical Space for Biology and Medicine","authors":"Christopher Lipinski","year":"2004","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ma et al., 2019] Tengfei Ma, Junyuan Shang, Cao Xiao, and Jimeng Sun. GENN: Predicting Correlated Drug-drug In- teractions with Graph Energy Neural Networks. CoRR, abs/1910.02107, 2019.","title":"GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks","authors":"Ma","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Matsugu et al., 2023] Shohei Matsugu, Yasuhiro Fujiwara, and Hiroaki Shiokawa. Uncovering the Largest Commu- nity in Social Networks at Scale. In Proceedings of the 32nd International Joint Conference on Artificial Intelli- gence, IJCAI2023, pages 2251-2260, 2023.","title":"Uncovering the Largest Community in Social Networks at Scale","authors":"Shohei Matsugu, Yasuhiro Fujiwara, Hiroaki Shiokawa","year":"2023","venue":"Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI2023","doi":null,"url":null,"internal_url":"/proceedings/2023/250"},{"position":20,"raw":"[McGibbon et al., 2023] Miles McGibbon, Sam Money- Kyrle, Vincent Blay, and Douglas R. Houston. SCORCH: Improving structure-based Virtual Screening with Ma- chine Learning Classifiers, Data Augmentation, and Un- certainty Estimation. Journal of Advanced Research, 46:135-147, 2023.","title":"SCORCH: Improving structure-based Virtual Screening with Machine Learning Classifiers, Data Augmentation, and Uncertainty Estimation","authors":"Miles Mcgibbon, Sam Money-Kyrle, Vincent Blay, Douglas R Houston","year":"2023","venue":"Journal of Advanced Research","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Mysinger et al., 2012] Michael M. Mysinger, Michael Carchia, John J. Irwin, and Brian K. Shoichet. Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. Journal of Medical Chemistry, 55(14):6582--6594, 2012.","title":"Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking","authors":"M Michael, Michael Mysinger, John J Carchia, Brian K Irwin, Shoichet","year":"2012","venue":"Journal of Medical Chemistry","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Naoi and Shiokawa, 2023] Yuma Naoi and Hiroaki Sh- iokawa. Boosting Similar Compounds Searches via Corre- lated Subgraph Analysis. In Proceedings of the 25th Inter- national Conference on Information Integration and Web Intelligence, iiWAS2023, pages 464-477, 2023.","title":"Yuma Naoi and Hiroaki Shiokawa. Boosting Similar Compounds Searches via Correlated Subgraph Analysis","authors":"Shiokawa Naoi","year":"2023","venue":"Proceedings of the 25th International Conference on Information Integration and Web Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Nguyen and Wei, 2019] Duc Duy Nguyen and Guo-Wei Wei. AGL-Score: Algebrac Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening. Journal of Chemical Information and Model- ing, 59:3291-3304, 2019.","title":"AGL-Score: Algebrac Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening","authors":"Duy Duc, Guo-Wei Nguyen, Wei","year":"2019","venue":"Journal of Chemical Information and Modeling","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Nguyen et al., 2020] Viet Khoa Tran Nguyen, Célien Jacquemard, and Didier Rognan. LIT-PCBA: An Unbi- ased Data Set for Machine Learning and Virtual Screen- ing. Journal of Chemical Information and Modeling, 60(9):4263-4273, 2020.","title":"LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening","authors":"Nguyen","year":"2020","venue":"Journal of Chemical Information and Modeling","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Nicklaus et al., 2012] Marc Nicklaus, Wolf-Dietrich Ihlen- feldt, Frank Oellien, Igor Filippov, and Markus Sitzmann. NCI Open Database Compounds. https://cactus.nci.nih. gov, 2012.","title":"NCI Open Database Compounds","authors":"Marc Nicklaus, Wolf-Dietrich Ihlenfeldt, Frank Oellien, Igor Filippov, Markus Sitzmann","year":"2012","venue":null,"doi":null,"url":"https://cactus.nci.nih.gov","internal_url":null},{"position":26,"raw":"[Pan and Zhu, 2012] Shirui Pan and Xingquan Zhu. CGStream: Continuous Correlated Graph Query for Data Streams. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM2012, page 1183-1192, 2012.","title":"CGStream: Continuous Correlated Graph Query for Data Streams","authors":"Zhu Pan, Shirui Pan, Xingquan Zhu","year":"2012","venue":"Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM2012","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Prateek et al., 2020] Arneish Prateek, Arijit Khan, Akshit Goyal, and Sayan Ranu. Mining Top-k Pairs of Correlated Subgraphs in a Large Network. Proceedings of VLDB En- dowment, 13(9):1511-1524, 2020.","title":"Mining Top-k Pairs of Correlated Subgraphs in a Large Network","authors":"Prateek","year":"2020","venue":"Proceedings of VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Reynolds, 1977] H. T. Reynolds. The Analysis of Cross- Classifications. The Free Press, New York, 1977.","title":"The Analysis of Cross-Classifications","authors":"H T Reynolds","year":"1977","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Samiullah et al., 2014] Md. Samiullah, Chowdhury Farhan Ahmed, Anna Fariha, Md. Rafiqul Islam, and Nicolas Lachiche. Mining Frequent Correlated Graphs with A New Measure. Expert Systems with Applications, 41(4, Part 2):1847-1863, 2014.","title":"Mining Frequent Correlated Graphs with A New Measure","authors":"Md, Chowdhury Samiullah, Anna Farhan Ahmed, Md Rafiqul Fariha, Nicolas Islam, Lachiche","year":"2014","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Shiokawa et al., 2013] Hiroaki Shiokawa, Yasuhiro Fuji- wara, and Makoto Onizuka. Fast Algorithm for Modularity-based Graph Clustering. In Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI2013, pages 1170-1176, 2013.","title":"Fast Algorithm for Modularity-based Graph Clustering","authors":"Hiroaki Shiokawa, Yasuhiro Fujiwara, Makoto Onizuka","year":"2013","venue":"Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI2013","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Shiokawa et al., 2019] Hiroaki Shiokawa, Toshiyuki Ama- gasa, and Hiroyuki Kitagawa. Scaling Fine-grained Mod- ularity Clustering for Massive Graphs. In Proceedings of the Twenty-Eighth International Joint Conference on Arti- ficial Intelligence, IJCAI-19, pages 4597-4604, 2019.","title":"Scaling Fine-grained Modularity Clustering for Massive Graphs","authors":"Hiroaki Shiokawa, Toshiyuki Amagasa, Hiroyuki Kitagawa","year":"2019","venue":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19","doi":null,"url":null,"internal_url":"/proceedings/2019/639"},{"position":32,"raw":"[Welke, 2020] Pascal Welke. Efficient Frequent Subgraph Mining in Transactional Databases. In Proceedings of 2020 IEEE 7th International Conference on Data Sci- ence and Advanced Analytics, DSAA2020, pages 307-314, 2020.","title":"Efficient Frequent Subgraph Mining in Transactional Databases","authors":"Pascal Welke","year":"2020","venue":"Proceedings of 2020 IEEE 7th International Conference on Data Science and Advanced Analytics","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yagi and Shiokawa, 2022] Ryuichi Yagi and Hiroaki Sh- iokawa. Fast Top-k Similar Sequence Search on DNA Databases. In Proceedings of the 24th International Con- ference on Information Integration and Web Intelligence, iiWAS2022, pages 145-150, 2022.","title":"Fast Top-k Similar Sequence Search on DNA Databases","authors":"Ryuichi Yagi, Hiroaki Shiokawa","year":"2022","venue":"Proceedings of the 24th International Conference on Information Integration and Web Intelligence","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Yagi et al., 2023] Ryuichi Yagi, Yuma Naoi, and Hiroaki Shiokawa. Fast Correlated DNA Subsequence Search via Graph-Based Representation. In Proceedings of the 25th International Conference on Information Integration and Web Intelligence, iiWAS2023, pages 339-347, 2023.","title":"Fast Correlated DNA Subsequence Search via Graph-Based Representation","authors":"Ryuichi Yagi, Yuma Naoi, Hiroaki Shiokawa","year":"2023","venue":"Proceedings of the 25th International Conference on Information Integration and Web Intelligence","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yan and Han, 2002] Xifeng Yan and Jiawei Han. gSpan: Graph-based Substructure Pattern Mining. In Proceedings of the 2002 IEEE International Conference on Data Min- ing, ICDM2002, pages 721-724, 2002.","title":"gSpan: Graph-based Substructure Pattern Mining","authors":"Yan, Han ; ] Xifeng Yan, Jiawei Han","year":"2002","venue":"Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM2002","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yang et al., 2022] Tianyi Yang, Li Han, and Shuanghong Huo. Dynamics and Allosteric Information Pathways of Unphosphorylaed c-Cbl. Journal of Chemical Information and Modeling, 62:6148-6159, 2022.","title":"Dynamics and Allosteric Information Pathways of Unphosphorylaed c-Cbl","authors":"Tianyi Yang, Li Han, Shuanghong Huo","year":"2022","venue":"Journal of Chemical Information and Modeling","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhang et al., 2022] Peiliang Zhang, Ziqi Wei, Chao Che, and Bo Jin. DeepMGT-DTI: Transformer Network Incor- porating Multilayer Graph Information for Drug-target In- teraction Prediction. Computers in Biology and Medicine, 142:105214, 2022.","title":"DeepMGT-DTI: Transformer Network Incorporating Multilayer Graph Information for Drug-target Interaction Prediction","authors":"Zhang","year":"2022","venue":"Computers in Biology and Medicine","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zou et al., 2009] Lei Zou, Lei Chen, and Yansheng Lu. Top-K Correlation Sub-graph Search in Graph Databases. In Proceedings of the 14th International Conference on Database Systems for Advanced Applications, DAS- FAA2009, pages 168-185, 2009.","title":"Top-K Correlation Sub-graph Search in Graph Databases","authors":"Lei Zou, Lei Chen, Yansheng Lu","year":"2009","venue":"Proceedings of the 14th International Conference on Database Systems for Advanced Applications, DAS-FAA2009","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}