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Finding Representative Nodes in Probabilistic Graphs [chapter]

Laura Langohr, Hannu Toivonen
2012 Lecture Notes in Computer Science  
We introduce the problem of identifying representative nodes in probabilistic graphs, motivated by the need to produce different simple views to large networks.  ...  We define a probabilistic similarity measure for nodes, and then apply clustering methods to find groups of nodes. Finally, a representative is output from each cluster.  ...  Algorithm 1 Find representative nodes Input: Set S of nodes, graph G, number k of representatives Output: k representative nodes from S 1: Find k clusters of nodes in S using similarities s(·) in graph  ... 
doi:10.1007/978-3-642-31830-6_15 fatcat:7l2b657tjzbo3d3ialrzmv2mpe

A diagnostic method that uses causal knowledge and linear programming in the application of Bayes' formula

Gregory F. Cooper
1986 Computer Methods and Programs in Biomedicine  
One assumption that is often made in the application of the formula is that the findings in a case are conditionally independent.  ...  This paper discusses a method for using causal knowledge to structure findings according to their probabilistic dependencies.  ...  The primary assumption in using such a graph is that it represents all the significant probabilistic relationships among the findings and etiologies.  ... 
doi:10.1016/0169-2607(86)90024-6 pmid:3519071 fatcat:c5e7gdkwd5bxbf2gov4jis3754

Research on Network Security Quantitative Model Based on Probabilistic Attack Graph

Yimin Cui, Junmei Li, Wei Zhao, Cheng Luan, N. Bardis
2019 ITM Web of Conferences  
Based on the attribute attack graph, the probabilistic attack graph model is generated by adding various factors which affect network security.  ...  In order to identify the threat of computer network security and evaluate its fragility comprehensively, the related factors of network security are studied, and the methods based on attack graph are improved  ...  index by probabilistic attack graph. The former scholars' models have been improved, mainly in the structure and the node type.  ... 
doi:10.1051/itmconf/20192402003 fatcat:zrze2foxv5gtfn6jxpjfmar5dm

Mining Uncertain Graphs: An Overview [chapter]

Vasileios Kassiano, Anastasios Gounaris, Apostolos N. Papadopoulos, Kostas Tsichlas
2017 Lecture Notes in Computer Science  
Formally, a probabilistic graph G is a triplet (V , E, p) where V is the set of nodes, E is the set of edges and p : E → (0, 1].  ...  Graphs play an important role in modern world, due to their widespread use for modeling, representing and organizing linked data.  ...  For probabilistic graphs, IPR is defined considering a weighted probabilistic graph G = (V, E, W, P ), where W represents the proximity between nodes in the graph.  ... 
doi:10.1007/978-3-319-57045-7_6 fatcat:lp5wktgocba3zhr5jww4bmjom4

Patterns and Logic for Reasoning with Networks [chapter]

Angelika Kimmig, Esther Galbrun, Hannu Toivonen, Luc De Raedt
2012 Lecture Notes in Computer Science  
While Biomine is based on graphs, ProbLog's core language is that of the logic programming language Prolog.  ...  This chapter provides an overview of important concepts, terminology, and reasoning tasks addressed in the two systems.  ...  The most likely answer Non-Redundant Set of Representatives Finding a non-redundant set of representatives as proposed in [10] consists in solving the representative nodes problem (cf.  ... 
doi:10.1007/978-3-642-31830-6_9 fatcat:mj3wxr6ldjbmtfieyde4sk4dra

Probabilistic loop scheduling for applications with uncertain execution time

S. Tongsima, E.H.-M. Sha, C. Chantrapornchai, D.R. Surma, N.L. Passos
2000 IEEE transactions on computers  
The relationship between these tasks can be represented as a data-flow graph where each node models the task associated with a probabilistic computation time.  ...  A set of edges represents the dependencies between tasks. In this research, we study scheduling and optimization algorithms taking into account the probabilistic execution times.  ...  This graph, called the probabilistic task-assignment graph (PTG), represents a schedule under the probabilistic model. Definition 4.1.  ... 
doi:10.1109/12.822565 fatcat:mhwvd2ylqvbk5f5g7xnkoivqta

An Efficient Approximation of Betweenness Centrality for Uncertain Graphs

Chenxu Wang, Ziyuan Lin
2019 IEEE Access  
Betweenness centrality measures the centrality of nodes and edges in a graph based on the concept of shortest paths.  ...  In the possible-world semantics, the Monte Carlo method is proposed to estimate the betweenness centrality of uncertain graphs. However, this method is computationally intensive.  ...  As a consequence, finding all possible shortest paths between two nodes in a probabilistic graph is also a #Phard problem.  ... 
doi:10.1109/access.2019.2915974 fatcat:eufp2d56hfcn3mgqptzjrce57y

Semantic Link Prediction through Probabilistic Description Logics

Kate Revoredo, José Eduardo Ochoa Luna, Fábio Gagliardi Cozman
2011 International Semantic Web Conference  
Predicting potential links between nodes in a network is a problem of great practical interest.  ...  In this paper, we propose an algorithm for link prediction that uses a probabilistic ontology described through the probabilistic description logic crALC.  ...  Note that graph adjacency allow us to address probabilistic inference for promising nodes.  ... 
dblp:conf/semweb/RevoredoLC11 fatcat:w2yislsmrzaglp4mmwt4lzvnvi

An Exact Approach to Learning Probabilistic Relational Model

Nourhene Ettouzi, Philippe Leray, Montassar Ben Messaoud
2016 European Workshop on Probabilistic Graphical Models  
Their construction represents an active area since it remains the most complicated issue.  ...  Probabilistic Relational Models (PRMs) extend BNs to work with relational databases rather than propositional data.  ...  Our relational order graph represents our solution space, where the BFHS is applied to find the shortest path and deduce then an optimal PRM.  ... 
dblp:conf/pgm/EttouziLM16 fatcat:oyj6djhl6vgxlbqs2xl3innr2u

Risk Prediction for Production of an Enterprise

Kumar Ravi, Sheopujan Singh
2013 International Journal of Computer Applications Technology and Research  
Bayesian network provides the feature to represent the probabilistic uncertainty and reasoning about probabilistic knowledge base, which is used here to represent the probable risks behind each causes  ...  For this, Multi-Entity Bayesian Network (MEBN) has been used to represent the requirements for production management as well as to assess the risks adherence in production management, where MEBN combines  ...  Predicate of the OWL will represent dependence of successor nodes on predecessor nodes, which is represented in OWL as <rdfs:dependsOn>.  ... 
doi:10.7753/ijcatr0203.1006 fatcat:t7jh5olbijdnti6ocjvzibakwu

Challenges on Probabilistic Modeling for Evolving Networks [article]

Jianguo Ding, Pascal Bouvry
2013 arXiv   pre-print
This paper presents a survey on probabilistic modeling for evolving networks and identifies the new challenges which emerge on the probabilistic models and optimization strategies in the potential application  ...  Due to the complexity of emerging networks, it is not always possible to build precise models in modeling and optimization (local and global) for networks.  ...  There is a strong correlation between finding patterns in static graphs and dynamic evolving graphs.  ... 
arXiv:1304.7820v2 fatcat:qvtskgtvpvh2npqbyjlghyu774

Fault Localization for Java Programs using Probabilistic Program Dependence Graph [article]

A. Askarunisa, T. Manju, B. Giri Babu
2012 arXiv   pre-print
In the proposed method Model Based Fault Localization Technique is used, which is called Probabilistic Program Dependence Graph .  ...  The PPDG construction augments the structural dependences represented by a program dependence graph with estimates of statistical dependences between node states, which are computed from the test set.  ...  It is used to find the probabilistic distribution of each node.  ... 
arXiv:1201.3985v1 fatcat:ykqhx3je5zdx7gtr7lwmbjre7m

Probabilistic Graph Programs for Randomised and Evolutionary Algorithms [chapter]

Timothy Atkinson, Detlef Plump, Susan Stepney
2018 Lecture Notes in Computer Science  
Third, we apply probabilistic graph programming to evolutionary algorithms working on graphs; we benchmark odd-parity digital circuit problems and show that our approach significantly outperforms the established  ...  We extend the graph programming language GP 2 with probabilistic constructs: (1) choosing rules according to user-defined probabilities and (2) choosing rule matches uniformly at random.  ...  The first example is Karger's randomised algorithm for finding a minimum cut in a graph [12] .  ... 
doi:10.1007/978-3-319-92991-0_5 fatcat:n27rzvtg65bfre4jdhe2rw6a34

Adaptive probabilistic Skip Graph

Amit Goyal
2015 2015 IEEE International Advance Computing Conference (IACC)  
Insertion and Deletion in Adaptive Probabilistic Skip Graph The steps followed in the insertion of a node in adaptive probabilistic skip graph are same as in skip graph with an additional initialization  ...  Search in Adaptive Probabilistic Skip Graph Searching in adaptive probabilistic skip graph is carried out as it was carried out earlier.  ...  Appendix A: Membership Vector of 1024 nodes 7 111001100 32 000011011 57 110011110 82 001100000 8 001000010 33 111100100 58 000110100 83 110011111 9 110111101 34 011100110 59 111001011  ... 
doi:10.1109/iadcc.2015.7154845 fatcat:rb65f7twjzem5a6ugv7meowwqu

Active Graph Matching Based on Pairwise Probabilities between Nodes [chapter]

Xavier Cortés, Francesc Serratosa, Albert Solé-Ribalta
2012 Lecture Notes in Computer Science  
We propose a method to perform active graph matching in which the active learner queries one of the nodes of the first graph and the oracle feedback is the corresponding node of the other graph.  ...  The method uses any graph matching algorithm that iteratively updates a probability matrix between nodes (Graduated Assignment, Expectation Maximisation or Probabilistic Relaxation).  ...  Figure 1 represents the probabilistic graph-matching paradigm. Fig. 1.  ... 
doi:10.1007/978-3-642-34166-3_11 fatcat:t5sgh2iqgbel7cs2xvbepbpoyq
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