A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Reverse Ranking by Graph Structure: Model and Scalable Algorithms
[article]
2016
arXiv
pre-print
Preserved Fulltext
Other Versions
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
2016
Performance Evaluation Review
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2012
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2012
Cyber Sensing 2012
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2014
2014 IEEE International Conference on Big Data (Big Data)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2020
Proceedings of The Web Conference 2020
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2008.07956v1 fatcat:k6xcs6xdfzc63d4he3qdy3e3mu
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]
2010
arXiv
pre-print
Preserved Fulltext
The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf.
Other Versions
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]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2012
2012 IEEE 28th International Conference on Data Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2012
2012 IEEE 28th International Conference on Data Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 11,346 results