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Reverse Ranking by Graph Structure: Model and Scalable Algorithms [article]

Eliav Buchnik, Edith Cohen
2016 arXiv   pre-print
We complement our algorithms by establishing the hardness of computing exact reverse-ranks for a single source and exact reverse-rank influence.  ...  That is, by the rank of u in an ordering of nodes by increasing distance from v. We identify and address fundamental challenges in rank-based graph mining.  ...  We identify and motivate fundamental challenges and present scalable algorithmic tools which facilitating rank-based graph mining.  ... 
arXiv:1506.02386v2 fatcat:aaij353tdvewnpbo7i7ctnccwm

Reverse Ranking by Graph Structure

Eliav Buchnik, Edith Cohen
2016 Performance Evaluation Review  
We complement our algorithms by establishing the hardness of computing exact reverse-ranks for a single source and exact reverse-rank influence.  ...  That is, by the rank of u in an ordering of nodes by increasing distance from v. We identify and address fundamental challenges in rank-based graph mining.  ...  The authors would like to thank Shiri Chechik and Amos Fiat for discussions and pointers.  ... 
doi:10.1145/2964791.2901458 fatcat:34dfpt2iufd3bh6h2cqbm5zjly

From graphs to DAGs: a low-complexity model and a scalable algorithm [article]

Shuyu Dong, Michèle Sebag
2022 arXiv   pre-print
Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling.  ...  This paper presents a low-complexity model, called LoRAM for Low-Rank Additive Model, which combines low-rank matrix factorization with a sparsification mechanism for the continuous optimization of DAGs  ...  Acknowledgement The authors warmly thank Fujitsu Laboratories LTD who funded the first author, and in particular Hiroyuki Higuchi and Koji Maruhashi for many discussions.  ... 
arXiv:2204.04644v1 fatcat:qwqehpj2ivdo3pfytybnoubnny

Modeling Content and Users: Structured Probabilistic Representation and Scalable Inference Algorithms

Amr Ahmed
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
We formally describe our algorithm with elegant examples and empirically evaluate the scalability and utility of our ideas.  ...  The data structure is assumed to be accessible by every subroutine.  ...  The algorithm generates a candidate subsequence S i,l in the three loops in lines 3, 5, and 6 of Algorithm 5.1, which essentially generates all possible subsequences of all possible lengths from D.  ... 
doi:10.1145/2339530.2378373 fatcat:2tbhm22bujd5lcqx752epdb27e

Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification [article]

Blaž Škrlj, Jan Kralj, Janez Konc, Marko Robnik-Šikonja, Nada Lavrač
2021 arXiv   pre-print
The main advantages of the proposed Deep Node Ranking (DNR) algorithm are competitive or better classification performance, significantly higher learning speed and lower space requirements when compared  ...  Furthermore, it enables exploration of the relationship between symbolic and the derived sub-symbolic node representations, offering insights into the learned node space structure.  ...  This algorithm uses a hierarchy-like structure to measure node similarity at different scales and constructs a multilayer graph to encode structural similarities and generate structural context for nodes  ... 
arXiv:1902.03964v6 fatcat:zlwkh66cqrclpiydbsr2ckdcf4

Causal Structure Learning [article]

Christina Heinze-Deml, Marloes H. Maathuis, Nicolai Meinshausen
2017 arXiv   pre-print
We discuss several recently proposed structure learning algorithms and their assumptions, and compare their empirical performance under various scenarios.  ...  Graphical models can represent a multivariate distribution in a convenient and accessible form as a graph.  ...  (rank)PC and (rank)FCI The PC algorithm (Spirtes et al., 2000) is named after its inventors Peter Spirtes and Clark Glymour.  ... 
arXiv:1706.09141v1 fatcat:i7bue46rojex7kwdlwvucqcfty

Finding structures in large-scale graphs

Sang P. Chin, Elizabeth Reilly, Linyuan Lu, Igor V. Ternovskiy, Peter Chin
2012 Cyber Sensing 2012  
We herein propose a framework using advanced tools 1-6 from random graph theory and spectral graph theory to address the quantitative analysis of the structure and dynamics of large-scale networks.  ...  One of the most vexing challenges of working with graphical structures is that most algorithms scale poorly as the graph becomes very large.  ...  We seek to extend the work in 18 to further explore how this geometric graph representation improves the scalability of graph algorithms and analysis.  ... 
doi:10.1117/12.978069 fatcat:yck4evvwcva55lqrwm7evdefe4

HBGSim: A structural similarity measurement over heterogeneous big graphs

Jiazhen Nian, Shan Jiang, Yan Zhang
2014 2014 IEEE International Conference on Big Data (Big Data)  
In this paper, we propose a new similarity measurement called HBGSim based on the heterogeneous structured data. HBGSim combines both local and global features by a two-stage process.  ...  However, these measurements cannot take full advantage of the data structure as heterogenous graph gains increasing popularity.  ...  This work is supported by NSFC with Grant No. 61370054, and 973 Program with Grant No. 2014CB340401 and 2014CB340405.  ... 
doi:10.1109/bigdata.2014.7004465 dblp:conf/bigdataconf/NianJZ14 fatcat:oi45x4eaeffwpmy2djepzaqeza

Learning the Structure of Auto-Encoding Recommenders

Farhan Khawar, Leonard Poon, Nevin L. Zhang
2020 Proceedings of The Web Conference 2020  
In this paper, we introduce structure learning for autoencoder recommenders by taking advantage of the inherent item groups present in the collaborative filtering domain.  ...  However, existing autoencoder recommenders use fully-connected neural network layers and do not employ structure learning.  ...  We present more details on the scalability of Algorithm 1 in the section 5.7.  ... 
doi:10.1145/3366423.3380135 dblp:conf/www/KhawarPZ20 fatcat:5qvjhful6vekzhchba2k7353hi

Learning to Predict Combinatorial Structures [article]

Shankar Vembu
2010 arXiv   pre-print
The consequence is a new technique for designing and analysing probabilistic structured prediction models.  ...  For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold.  ...  ; Hazan et al., 2007) resulting in scalable algorithms for structured prediction.  ... 
arXiv:0912.4473v2 fatcat:olzo64uyxfg5jcjjvtjidvek54

Scalable Structure Learning for Probabilistic Soft Logic [article]

Varun Embar and Dhanya Sridhar and Golnoosh Farnadi and Lise Getoor
2018 arXiv   pre-print
We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways.  ...  The highly scalable optimization method combines data-driven generation of clauses with a piecewise pseudolikelihood (PPLL) objective that learns model structure by optimizing clause weights only once.  ...  Acknowledgements This work is sponsored by the Air Force Research Laboratory (AFRL) and Defense Advanced Research Projects Agency (DARPA), and supported by NSF grants CCF-1740850 and NSF IIS-1703331.  ... 
arXiv:1807.00973v1 fatcat:yohfv544hbdqvkflzuf7n6ph3q

A Structured Method for Compilation of QAOA Circuits in Quantum Computing [article]

Yuwei Jin, Jason Luo, Lucent Fong, Yanhao Chen, Ari B. Hayes, Chi Zhang, Fei Hua, Eddy Z. Zhang
2022 arXiv   pre-print
Prior studies lack the following: (1) Performance guarantee, (2) Scalability, and (3) Awareness of regularity in scalable hardware.  ...  We also demonstrate how our method runs on Google Sycamore and IBM Non-linear architectures in a scalable manner and in linear time.  ...  We performed experiments with three type of problem graphs: random graphs, regular graphs, and 2-local Hamitonian simulation graphs (for 1-D NN Ising model, 2-D NNN XY model, and 3-D NNN Heisenberg model  ... 
arXiv:2112.06143v4 fatcat:hdsgukzv5fcwtebscpy5zpon2q

Keyword Query Reformulation on Structured Data

Junjie Yao, Bin Cui, Liansheng Hua, Yuxin Huang
2012 2012 IEEE 28th International Conference on Data Engineering  
We first utilize a heterogenous graph to model the words and items in structured data, and design an enhanced Random Walk approach to extract relevant terms from the graph context.  ...  We propose an automatic keyword query reformulation approach by exploiting structural semantics in the underlying structured data sources.  ...  We believe this is an intriguing character of graph structure, which can model rich direct and indirect connections amongst structured data.  ... 
doi:10.1109/icde.2012.76 dblp:conf/icde/YaoCHH12 fatcat:a54eveakl5ckhg35rkz6uqg2z4

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs [article]

Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
2022 arXiv   pre-print
A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper.  ...  A learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the optimization of the graph structure and parameters through stochastic gradient descent  ...  The computational time is per run and per graph, in seconds. 100-variable artificial graphs Tables 6, and 8 show the scalability of SAM w.r.t. the number of variables.  ... 
arXiv:1803.04929v5 fatcat:ynkjkpqj6vdvplnkzfbjgntt24

On Top-k Structural Similarity Search

Pei Lee, Laks V.S. Lakshmanan, Jeffrey Xu Yu
2012 2012 IEEE 28th International Conference on Data Engineering  
Meeting-based methods including SimRank and P-Rank capture structural similarity very well.  ...  None of these approaches can handle top-k structural similarity search efficiently by scaling to very large graphs consisting of millions of nodes.  ...  TopSim Algorithms on Disk Resident Graphs: To test the scalability of TopSim family algorithms, we implement Trun-TopSim-SM and Prio-TopSim-SM for huge disk resident graphs.  ... 
doi:10.1109/icde.2012.109 dblp:conf/icde/LeeLY12 fatcat:xmqfvltghjfbhhiyqocbnm2xpm
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