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Average Sensitivity of Graph Algorithms
[article]
2020
arXiv
pre-print
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We formalize this idea by introducing the notion of average sensitivity of graph algorithms, which is the average earth mover's distance between the output distributions of an algorithm on a graph and ...
After deriving basic properties of average sensitivity such as composition, we provide efficient approximation algorithms with low average sensitivities for concrete graph problems, including the minimum ...
We are grateful to anonymous reviewers for suggesting a major improvement to the average sensitivity analysis of Algorithm 4. We thank Tasuku Soma and Samson Zhou for several helpful discussions. ...
arXiv:1904.03248v3
fatcat:v7ueemgxwzacfislauq7fpr33y
CogBoost: Boosting for Fast Cost-Sensitive Graph Classification
2015
IEEE Transactions on Knowledge and Data Engineering
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In this paper, we propose, CogBoost, a fast cost-sensitive graph classification algorithm, where the goal is to minimize the misclassification costs (instead of the errors) and to achieve fast learning ...
Experiments and comparisons on real-world large graph datasets demonstrate the effectiveness and the efficiency of our algorithm. ...
In this paper, we consider the unique challenges of graph data, and propose a novel algorithm for cost-sensitive graph classification. ...
doi:10.1109/tkde.2015.2391115
fatcat:qrrpkr7axfhmxfwd3oxzm6v6ie
Performance Analysis Of Learning Automata-Based Routing Algorithms In Sparse Graphs
2008
Zenodo
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The sensitivity of three main performance parameters of the algorithm, being average number of processed nodes, scanned edges and average time per update, to variation in graph scarcity is reported. ...
How ever, there has been little work on the effects of variation of graph scarcity on the performance of these algorithms. ...
The sensitivity of three main performance parameters of the algorithm, being average number of processed nodes, scanned edges and average time per update, to variation in graph scarcity is reported and ...
doi:10.5281/zenodo.1329209
fatcat:3e7zgijwanahzim3injops4rrq
Publishing Node Strength Distribution with Node Differential Privacy
2020
IEEE Access
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However, in the case of graph anonymity [1] [2] , the direct publication of graph data still has a high probability of disclosing personal sensitive information. ...
Proposed Projection Algorithm Although the current graph projection algorithm reduces the sensitivity, but it loses a lot of important information, and the existing algorithms can only be divided into ...
doi:10.1109/access.2020.3040077
fatcat:cn25vsz36bf7xavttxx2pvrmue
Page 3880 of Mathematical Reviews Vol. , Issue 97F
[page]
1997
Mathematical Reviews
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We show that for the family of functions considered by Rubinstein [op. cit.], the average block sensitivity is Q(\/n), while the average sensitivity is asymptotically zero. ...
It is related to the problem of computing a maximum weight Hamiltonian path in a graph. Some earlier ap- proximation algorithms for SSP are based on vertex disjoint paths in a related graph. ...
Rapid ab initio RNA Folding Including Pseudoknots Via Graph Tree Decomposition
[chapter]
2006
Lecture Notes in Computer Science
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Such a graph is tree decomposable; dynamic programming over a tree decomposition of the graph leads to an efficient algorithm. ...
It demonstrates overall sensitivity and specificity that outperforms or is comparable with those of previous optimal and heuristic algorithms yet it requires significantly less time than other optimal ...
For tRNA's, the average running time of 0.54 seconds for TdFOLD is slower than the average 0.03 of ILM and the average 0.41 of PKNOTS but faster than the average 3.33 of HotKnots. ...
doi:10.1007/11851561_25
fatcat:vs5pg5yv3zazfe7k5ohkc6ryx4
Average Sensitivity of Spectral Clustering
2020
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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To make reliable and eicient decisions based on spectral clustering, we assess the stability of spectral clustering against edge perturbations in the input graph using the notion of average sensitivity ...
We irst prove that the average sensitivity of spectral clustering is proportional to λ 2 /λ 2 3 , where λ i is the i-th smallest eigenvalue of the (normalized) Laplacian. ...
Average Sensitivity In order to measure the sensitivity of spectral clustering algorithms, which partition the vertex set of a graph into k clusters for k ≥ 2, we adapt the original deinition of average ...
doi:10.1145/3394486.3403166
fatcat:kz75wijfc5d6njjwojak5b2nsm
Average Sensitivity of Spectral Clustering
[article]
2020
arXiv
pre-print
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To make reliable and efficient decisions based on spectral clustering, we assess the stability of spectral clustering against edge perturbations in the input graph using the notion of average sensitivity ...
We first prove that the average sensitivity of spectral clustering is proportional to λ_2/λ_3^2, where λ_i is the i-th smallest eigenvalue of the (normalized) Laplacian. ...
Average Sensitivity In order to measure the sensitivity of spectral clustering algorithms, which partition the vertex set of a graph into k clusters for k ≥ 2, we adapt the original definition of average ...
arXiv:2006.04094v1
fatcat:el2szvwbwza5jaiqrbptsqhjl4
Sensitivity Analysis of the Maximum Matching Problem
[article]
2020
arXiv
pre-print
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) that obtains average sensitivity n^O(1/(1+ϵ^2)) sensitivity algorithm, and show a deterministic 1/2-approximation algorithm with sensitivity (O(log^*n)) for bounded-degree graphs. ...
We consider the sensitivity of algorithms for the maximum matching problem against edge and vertex modifications. ...
Average sensitivity of graph algorithms. ...
arXiv:2009.04556v1
fatcat:rylbe7pxvbahhhc5m3xh3vqpam
Social Networking With Protecting Sensitive Labels Using Anonymization Methodology
2016
Zenodo
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User�s privacy models get forced by alternative user, if a bunch of node for the most part shares similar sensitive labels then new users simply establish recent user�s information, so solely structure ...
User�s privacy models get forced by alternative user, if a bunch of node for the most part shares similar sensitive labels then new users simply establish recent user�s information, so solely structure ...
Average Change of Sensitive Label Path Length (ACSPL) and Remaining ratio of top influential users (RRTI) will be calculated. ...
doi:10.5281/zenodo.1468486
fatcat:5pd7gfumjba45jsx37bprjpq3m
Private Analysis of Graph Structure
2014
ACM Transactions on Database Systems
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We give explicit, simple conditions under which these algorithms add a small amount of noise. We also provide the average-case analysis in the Erdős-Rényi-Gilbert G(n, p) random graph model. ...
We also give algorithms for k-triangle queries using a different approach, based on the higher-order local sensitivity. ...
Thus, an average-case analysis of our algorithms is necessarily limited by the choice of probability model. ...
doi:10.1145/2611523
fatcat:hg7447vlyrcp5lqg7fec77vakm
State-Sensitive Points-to Analysis for the Dynamic Behavior of JavaScript Objects
[chapter]
2014
Lecture Notes in Computer Science
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The algorithm represents objects by their creation sites and local property names; it tracks property updates via a new control-flow graph representation. ...
We compare the new points-to algorithm with an existing JavaScript points-to algorithm in terms of their respective performance and accuracy on a client application. ...
Block-Sensitive Analysis Our new points-to analysis algorithm is a fixed point calculation on the call graph, initialized with an empty points-to graph on entry to the JavaScript program, in which every ...
doi:10.1007/978-3-662-44202-9_1
fatcat:wcgljbeclzavtcbkdivg4fmi3y
Pressure Sensor Placement in Water Supply Network Based on Graph Neural Network Clustering Method
2022
Water
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A new method of pressure sensor placement is proposed in this paper based on Graph Neural Networks. ...
The majority of existing studies focuses on sensitivity or burst identification ability of monitoring systems based on certain specific operating conditions of WSNs, while nodal connectivity or long-term ...
Acknowledgments: Thanks to National Key R & D Program of China for supporting and funding this project, grant number 2016YFC0802400.
Conflicts of Interest: No conflict of interest. ...
doi:10.3390/w14020150
fatcat:dk36k7g3ijeijcb3y7h6oy2nyq
Gabor-based Orthogonal Locality Sensitive Discriminant Analysis for face recognition
2008
2008 9th International Conference on Signal Processing
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Our algorithm is based on the Locality Sensitive Discriminant Analysis (LSDA) algorithm, which aims at finding a projection by maximizing the margin between data points from different classes at each local ...
Non-orthogonality distorts the local geometrical structure of the data submanifold. ...
graph G w and S b,ij of graph G b model the local and discriminant structure of the face manifold. 4. ...
doi:10.1109/icosp.2008.4697447
fatcat:mncex6ip7ngupaym4enw5bdwyi
Cost-Efficient Allocation of Additional Resources for the Service Placement Problem in Next-Generation Internet
2015
Mathematical Problems in Engineering
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To this end, we (1) introduce a new concept of sensitivity for each service node to locate the bottleneck node, (2) state the problem of allocating additional resources, and (3) use sensitivity to propose ...
One of the major challenges in next-generation Internet is to allocate services to nodes in the network. This problem, known as theservice placement problem, can be solved by layered graph approach. ...
The main task of this algorithm (Algorithm 1) is to set up the layered graph and run the capacity tracking algorithm known as layered graph with capacity tracking (LG-CT) to record the performance metrics ...
doi:10.1155/2015/517409
fatcat:b2ho2r7njfh7vlyjyh6s7esnda
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