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Time-evolving graph processing at scale

Anand Padmanabha Iyer, Li Erran Li, Tathagata Das, Ion Stoica
2016 Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems - GRADES '16  
Unbounded, real-time data is fast becoming the norm [2], and thus it is important to process these time-evolving graph-structured datasets efficiently.  ...  We then introduce G T , a time-evolving graph processing framework built on top of Apache Spark, a widely used distributed dataflow system.  ...  In this paper, we present G T , a system for time-evolving graph processing that is built on a dataflow framework.  ... 
doi:10.1145/2960414.2960419 dblp:conf/grades/IyerLDS16 fatcat:tks4gkhimzhtzocriqu3vxwrle

Representation Learning over Dynamic Graphs [article]

Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha
2018 arXiv   pre-print
These processes exhibit complex nonlinear dynamics that evolve at different time scales and subsequently contribute to the update of node embeddings.  ...  We employ a time-scale dependent multivariate point process model to capture these dynamics.  ...  Further, as the processes evolve at multiple time scales, we extend the point process model to consider a time scale dependent parameter.  ... 
arXiv:1803.04051v2 fatcat:gcgfprjh6fhpbhgcegbou4aiee

Generating Mechanisms for Evolving Software Mirror Graph

Ling-Zan Zhu, Bei-Bei Yin, Kai-Yuan Cai
2012 Journal of Modern Physics  
To explain how the software mirror graph evolves into a small world and scale free structure, in this paper we further proposed a mathematical model based on the mechanisms of growth, preferential attachment  ...  Different from the perspectives adopted in these works, our previous work proposed software mirror graph to model the dynamic execution processes of software and revealed software mirror graph may also  ...  Denote the software mirror graph at time t as , , SM t t t t G V E A  { , , , } v v v  1 . It represents the software state at time t and looks like a mirror of the software state at time t.  ... 
doi:10.4236/jmp.2012.39139 fatcat:dpf2xw62nrhwpizo5uvng6apei

Design of Control Structure for Integrated Process Networks Based on Graph-theoretic Analysis

L. Kang, Y. Liu
2017 Chemical Engineering Transactions  
flow graph and energy flow graph so that both the material and energy flow structures of the process networks in each time scale can be captured and analysed at the same time using a graph theory based  ...  The control structure of the network evolved in each time scale is then optimally designed by employing a graph-theoretic approach, which can be used as the basis for hierarchical control structure of  ...  The process units evolving in this time scale are P3 = {1,2,3,4,5,6,7,8,9,10,12,13,14} .  ... 
doi:10.3303/cet1761283 doaj:57f829e8fcc7411393e99a0725259f05 fatcat:s7yia2rc4bc35fanixfyderpiy

Graph-theoretic Analysis of Complex Energy Integrated Networks*

Sujit S Jogwar, Srinivas Rangarajan, Prodromos Daoutidis
2013 IFAC Proceedings Volumes  
In previous work, we have developed a graph-theoretic framework to systematically uncover this time scale multiplicity.  ...  This system involves energy flows spanning three orders of magnitude and the underlying energy balance variables evolve over two time scales.  ...  All the nodes in this subgraph evolve at the time scale τ m .  ... 
doi:10.3182/20131218-3-in-2045.00058 fatcat:7e53cb3545aftatatobjr7nwsi

Reduction of complex energy-integrated process networks using graph theory

Sujit S. Jogwar, Srinivas Rangarajan, Prodromos Daoutidis
2015 Computers and Chemical Engineering  
The modular structure of these complex networks lends them to a graph theoretic analysis, whereby weak and strong connections between process units arising from time scale separation are identified from  ...  This paper focuses on the analysis of complex (multi-loop) energyintegrated process networks.  ...  scale as the total enthalpy evolves in the slower time scale.  ... 
doi:10.1016/j.compchemeng.2015.04.025 fatcat:w7fdv6gqprcprl67bjb6nwps4q

Real-Time Diameter Monitoring for Time-Evolving Graphs [chapter]

Yasuhiro Fujiwara, Makoto Onizuka, Masaru Kitsuregawa
2011 Lecture Notes in Computer Science  
Our solution, G-Scale, can track the diameter of time-evolving graphs in the most efficient and correct manner.  ...  The goal of this work is to identify the diameter, the maximum distance between any two nodes, of graphs that evolve over time.  ...  -Exact: G-Scale does not sacrifice accuracy even though it prunes unlikely nodes in the monitoring process; it can exactly track the node pair that delimit the diameter of a time-evolving graph at any  ... 
doi:10.1007/978-3-642-20149-3_24 fatcat:iejyscxl4bhitp5pvb5noxzyx4

Challenges on Probabilistic Modeling for Evolving Networks [article]

Jianguo Ding, Pascal Bouvry
2013 arXiv   pre-print
areas of network performance, network management and network security for evolving networks.  ...  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  ...  Temporal Networks Evolving is a time correlated process.  ... 
arXiv:1304.7820v2 fatcat:qvtskgtvpvh2npqbyjlghyu774

A Local Approximation Approach for Processing Time-Evolving Graphs

Shuo Ji, Yinliang Zhao
2018 Symmetry  
To efficiently process time-evolving graphs where new vertices and edges are inserted over time, an incremental computing model, which processes the newly-constructed graph based on the results of the  ...  Then, we develop an optimization approach to reduce the response time in bulk synchronous parallel (BSP)-based incremental computing systems by processing time-evolving graphs on the local graph structure  ...  performance of time-evolving graph processing.  ... 
doi:10.3390/sym10070247 fatcat:zboe2scwefbaxh3lf62fugfd3u

A matter of time - intrinsic or extrinsic - for diffusion in evolving complex networks

Alice Albano, Jean-Loup Guillaume, Sébastien Heymann, Bénédicte Le Grand
2013 Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13  
Many studies aim at investigating diffusion as an evolving phenomenon but mostly occurring on static networks, and much remains to be done to understand diffusion on evolving networks.  ...  new link in the graph.  ...  Many studies aim at investigating diffusion as an evolving phenomenon but mostly occurring on static networks [3] , although most real-world networks evolve over time with the creation of new nodes or  ... 
doi:10.1145/2492517.2492634 dblp:conf/asunam/AlbanoGHG13 fatcat:s7brzgh5zncbpdxqrojhvgpsey

Evolving network – simulation study

D. Makowiec
2005 European Physical Journal B : Condensed Matter Physics  
The evolving system self-organizes into stationary states. The topological transition in the graph structure is noticed with respect to p.  ...  There exist conditions at which the resulting stationary network ensemble provides networks which degree distribution exhibit power-law decay in large interval of degrees.  ...  Our focus is on the process of graph restructuring.  ... 
doi:10.1140/epjb/e2006-00008-2 fatcat:5kbsdnqdmreu3hrxui4r4kov2y

Analyzing Complex Data in Motion at Scale with Temporal Graphs

Thomas Hartmann, Francois Fouquet, Matthieu Jimenez, Romain Rouvoy, Yves Le Traon
2017 Proceedings of the 29th International Conference on Software Engineering and Knowledge Engineering  
Temporal graphs provide a compact representation of time-evolving graphs that can be used to analyze complex data in motion.  ...  This success challenges analytics algorithms to deal with more and more complex data, which can be structured as graphs and evolve over time.  ...  Temporal graph processing frameworks go a step further and consider time-evolving graphs.  ... 
doi:10.18293/seke2017-048 dblp:conf/seke/0001FJRT17 fatcat:hixftbs4onboxgr7z7xjk2bw4i

Cellular Automata on Graphs: Topological Properties of ER Graphs Evolved towards Low-Entropy Dynamics

Carsten Marr, Marc-Thorsten Hütt
2012 Entropy  
Totalistic cellular automata, where the update rules depend only on the density of neighboring states, are at the same time a versatile tool for exploring dynamical processes on graphs.  ...  We then extend the investigation towards graphs obtained in a simulated-evolution procedure, starting from Erdős-Rényi (ER) graphs and selecting for low entropies of the CA dynamics.  ...  Metabolic networks are at the same time scale-free, modular, layered and bipartite.  ... 
doi:10.3390/e14060993 fatcat:ygsi2lm27zfmlgrw7w2ijbvb2q

Influence Maximization over Markovian Graphs: A Stochastic Optimization Approach [article]

Buddhika Nettasinghe, Vikram Krishnamurthy
2017 arXiv   pre-print
This paper considers the problem of randomized influence maximization over a Markovian graph process: given a fixed set of nodes whose connectivity graph is evolving as a Markov chain, estimate the probability  ...  Further, it is assumed that the sampling process affects the evolution of the graph i.e. the sampling distribution and the transition probability matrix are functionally dependent.  ...  Henceforth, n is used to denote the discrete time scale on which the Markovian graph process evolves.  ... 
arXiv:1711.03327v1 fatcat:qu2vtwet7rfxtcg4nvgke7btlq

Evolutionary Design and Assembly Planning for Stochastic Modular Robots [chapter]

Michael T. Tolley, Jonathan D. Hiller, Hod Lipson
2011 Studies in Computational Intelligence  
Each sample begins with the final completed structure and removes one accessible component at a time until the existing substructure is recovered.  ...  Here we present a process that automatically evolves target structures based on functional requirements and plans the error-free assembly of these structures from a large number of active components.  ...  The time taken to assemble a structure in simulation scales linearly with the number of modules while the total time taken to compute the locations to next attract modules at each stage scales with the  ... 
doi:10.1007/978-3-642-18272-3_14 fatcat:6mxxthzwmbfpxnqcou56qmot6a
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