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Hypergraph Co-Optimal Transport: Metric and Categorical Properties [article]

Samir Chowdhury, Tom Needham, Ethan Semrad, Bei Wang, Youjia Zhou
2022 arXiv   pre-print
First, we introduce a hypergraph distance based on the co-optimal transport framework of Redko et al. and study its theoretical properties.  ...  Finally, we demonstrate the versatility of our Hypergraph Co-Optimal Transport (HyperCOT) framework through various examples.  ...  This work was partially supported by NSF DMS 2107808, NSF IIS-1910733, and DOE DE-SC0021015.  ... 
arXiv:2112.03904v2 fatcat:3vntsro5srf5rkox6acq2jwu7m

Hypergraph Co-Optimal Transport: Metric and Categorical Properties [article]

Samir Chowdhury, Tom Needham, Ethan Semrad, Bei Wang, Youjia Zhou
2021
First, we introduce a hypergraph distance based on the co-optimal transport framework of Redko et al. and study its theoretical properties.  ...  Finally, we demonstrate the versatility of our Hypergraph Co-Optimal Transport (HyperCOT) framework through various examples.  ...  . • We extend the co-optimal transport framework of Redko et al. [32] to define an optimal transport-based distance between hypergraphs.  ... 
doi:10.48550/arxiv.2112.03904 fatcat:shzap67dcvcxtcuj5m4un2bs7m

Overlapping Community Extraction: A Link Hypergraph Partitioning Based Method

Haicheng Tao, Zhiang Wu, Jin Shi, Jie Cao, Xiaofeng Yu
2014 2014 IEEE International Conference on Services Computing  
Third, we propose to use the hypergraph to assemble all local link structures, and employ hMETIS for hypergraph partitioning.  ...  Second, based upon our prior work, we transform the problem of mining local link structures into a pattern mining problem, and thus present an efficient mining algorithm.  ...  Note that the existing overlapping community detection methods were roughly categorized into four classes [4] (e.g., clique percolation, link partitioning, local expansion and optimization, and fuzzy  ... 
doi:10.1109/scc.2014.25 dblp:conf/IEEEscc/TaoWSCY14 fatcat:l2c5klg5zjbbtkorgwdzeec4dq

Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds

Tingran Gao, Shahab Asoodeh, Yi Huang, James Evans
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
e.g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation.  ...  Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating "soft labels" (  ...  transport (Villani 2003; , on graphs and hypergraphs.  ... 
doi:10.1609/aaai.v33i01.33013630 fatcat:udglxkl6rvbangtcxgbpvexgle

Hypergraph Discretization of the Cauchy Problem in General Relativity via Wolfram Model Evolution [article]

Jonathan Gorard
2021 arXiv   pre-print
(both rotating and non-rotating), and explicitly illustrate the relationship between the discrete hypergraph topology and the continuous Riemannian geometry that is being approximated.  ...  The Wolfram model offers an inherently discrete formulation of the Einstein field equations as an a priori Cauchy problem, in which Cauchy initial data is specified on a single spatial hypergraph, and  ...  Acknowledgments The author would like to thank Stephen Wolfram for his continual encouragement in the pursuit of the present project, as well as for useful conversations and suggestions.  ... 
arXiv:2102.09363v2 fatcat:6pxav3qjsne6tjee6k62dq7k5u

Neural Predicting Higher-order Patterns in Temporal Networks

Yunyu Liu, Jianzhu Ma, Pan Li
2022 Proceedings of the ACM Web Conference 2022  
HIT extracts the structural representation of a node triplet of interest on the temporal hypergraph and uses it to tell what type of, when, and why the interaction expansion could happen in this triplet  ...  This posts us the challenge of designing more sophisticated hypergraph models for these higher-order patterns and the associated new learning algorithms.  ...  Liu and P. Li are supported by the 2021 JPMorgan Faculty Award and the National Science Foundation (NSF) award HDR-2117997.  ... 
doi:10.1145/3485447.3512181 fatcat:sfr6izphsbc5vaklz2ooybg37e

Neural Predicting Higher-order Patterns in Temporal Networks [article]

Yunyu Liu, Jianzhu Ma, Pan Li
2022 arXiv   pre-print
HIT extracts the structural representation of a node triplet of interest on the temporal hypergraph and uses it to tell what type of, when, and why the interaction expansion could happen in this triplet  ...  This posts us the challenge of designing more sophisticated hypergraph models for these higher-order patterns and the associated new learning algorithms.  ...  Liu and P.L. are supported by the 2021 JPMorgan Faculty Award and the National Science Foundation (NSF) award HDR-2117997.  ... 
arXiv:2106.06039v2 fatcat:utac3kwnznc2nnwmhwhn3fess4

A Recursive Hypergraph Bipartitioning Framework for Reducing Bandwidth and Latency Costs Simultaneously

Oguz Selvitopi, Seher Acer, Cevdet Aykanat
2016 IEEE Transactions on Parallel and Distributed Systems  
However, both volume-and message-related metrics should be taken into account during partitioning for a more efficient parallelization.  ...  In this work, we propose a recursive hypergraph bipartitioning framework that reduces the total volume and total message count in a single phase.  ...  ACKNOWLEDGMENTS This work is partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under project EEEAG-114E545.  ... 
doi:10.1109/tpds.2016.2577024 fatcat:2w5tcebcevfxrn3um4vuf6muxe

The why, how, and when of representations for complex systems [article]

Leo Torres, Ann S. Blevins, Danielle S. Bassett, Tina Eliassi-Rad
2020 arXiv   pre-print
observed data (i.e. graphs, simplicial complexes, and hypergraphs), and relevant computational methods for each formalism.  ...  At each step we consider different types of dependencies; these are properties of the system that describe how the existence of one relation among the parts of a system may influence the existence of another  ...  /woman, 0% non-binary , and 19% unknown categorization.  ... 
arXiv:2006.02870v1 fatcat:6rp6iechmbdzdpdolgprr2vh7e

HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network [article]

Khaled Mohammed Saifuddin, Briana Bumgardner, Farhan Tanvir, Esra Akbas
2022 arXiv   pre-print
Then, we develop HyGNN consisting of a novel attention-based hypergraph edge encoder to get the representation of drugs as hyperedges and a decoder to predict the interactions between drug pairs.  ...  To capture the drug similarities, we create a hypergraph from drugs' chemical substructures extracted from the SMILES strings.  ...  During training, we simultaneously optimize the encoder and decoder using adam optimizer.  ... 
arXiv:2206.12747v3 fatcat:3ybbwvoxqfdgxiill3ztenorse

Exploring photosynthesis evolution by comparative analysis of metabolic networks between chloroplasts and photosynthetic bacteria

Zhuo Wang, Xin-Guang Zhu, Yazhu Chen, Yuanyuan Li, Jing Hou, Yixue Li, Lei Liu
2006 BMC Genomics  
The properties of the entire metabolic network and the sub-network that consists of reactions directly connected to the Calvin Cycle have been analyzed using hypergraph representation.  ...  network properties that are different from cyanobacteria and to analyze possible functional significance of those features.  ...  Hans Bohnert, and Dr. Peter Gogarten for their insightful discussion and comments.  ... 
doi:10.1186/1471-2164-7-100 pmid:16646993 pmcid:PMC1524952 fatcat:syata44srvae5jvqohrgv5lyay

Challenges and Limitations of Biological Network Analysis

Marianna Milano, Giuseppe Agapito, Mario Cannataro
2022 BioTech  
Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms' properties.  ...  Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis.  ...  Hypergraphs are an extension of graphs and multigraphs.  ... 
doi:10.3390/biotech11030024 pmid:35892929 pmcid:PMC9326688 fatcat:7f5zp3sczzfclbbz6ty3nyremu

Automatic parallelization of a class of irregular loops for distributed memory systems

Mahesh Ravishankar, John Eisenlohr, Louis-Noël Pouchet, J. Ramanujam, Atanas Rountev, P. Sadayappan
2014 ACM Transactions on Parallel Computing  
However these loops often contain data-dependent control-flow and array-access patterns. Traditional optimizations that rely on purely static analysis fail to generate parallel code in such cases.  ...  We also describe algorithms to generate a parallel inspector that performs a runtime analysis of control-flow and array-access patterns, and a parallel executor to take advantage of this information.  ...  Gagan Agrawal (CSE, OSU) and the reviewers for their comments and feedback with respect to existing inspector/executor techniques. We also thank Robert L.  ... 
doi:10.1145/2660251 fatcat:24ghpaagpzbmpgzt2bjb2wi364

Deep Graph Generators: A Survey

Faezeh Faez, Yassaman Ommi, Mahdieh Soleymani Baghshah, Hamid R. Rabiee
2021 IEEE Access  
We also present publicly available source codes, commonly used datasets, and the most widely utilized evaluation metrics.  ...  Finally, we review current trends and suggest future research directions based on the existing challenges.  ...  IMPLEMENTATIONS In this section, we discuss the implementation details by categorizing and summarizing commonly used datasets and evaluation metrics.  ... 
doi:10.1109/access.2021.3098417 fatcat:6xzg5cs75zhovdbpjkignfr3xu

Author Index

2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
Optimal Pixel Labeling Algorithms for Tree Metrics Tarsetti, Flavio Demo: MOBIO: Mobile Biometric Face and Speaker Authentication Taskar, Ben Object Detection via Boundary Structure Segmentation  ...  Metric-Induced Optimal Embedding for Intrinsic 3D Shape Analysis Diffeomorphic Sulcal Shape Analysis for Cortical Surface Registration Toledo, Ricardo Fast and Robust Object Segmentation with the  ... 
doi:10.1109/cvpr.2010.5539913 fatcat:y6m5knstrzfyfin6jzusc42p54
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