Search Results for "random walk"
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Showing 12 open source projects for "random walk"
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ChaosEsqueAnthology
A free 3d game for PC that does not limit the player. Fight,Build,Gain
(NOTE: Make sure to download Volume2 aswell!: http://sourceforge.net/p/chaosesqueanthologyvolume2/ ) 200+ weapons. Build-able enter-able buildings (Like an FPS RPG but with interiors) (Multi-tool/ Utili-tool). City generation (all buildings enterable). A 2 iso (6GB) exploration of what QuakeC can do. Over 100 maps included, along with map editor and sourcecode. Fully free and opensource: code and media. A modification of Xonotic which included extended weapons, maps, vehicles,... -
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RWRMTN
Predicting disease-associated miRNAs on a miRNA-target gene network
The misregulations of microRNA have been shown the contribution to diseases. Recently, we have proposed a computational method based on a random walk framework on a microRNA-target gene network to predict disease-associated microRNAs. This was shown superior when compared to existing state-of-the-art network- and machine learning-based methods since it well exploits mutual regulation between miRNAs and their target genes in microRNA-target gene networks. To facilitate the use of this method, we develop a Cytoscape app, named RWRMTN, to predict disease-associated microRNAs. ... -
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Kalibr Allan
IMU Allan standard deviation charts
...These plots are analyzed to determine noise density and random walk parameters for both gyroscopes and accelerometers. The repository also includes example data and plots from real sensors such as the XSENS MTI-G-700, Tango Yellowstone Tablet, and ASL-ETH VI-Sensor, providing reference points for interpretation. -
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Pi-Trav
An interactive pi walk.
A simulation and visualization of random walks based on the digits pi. This program also has a settings menu. -
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5
node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction.... -
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Community Detection Modularity Suite
Suite of community detection algorithms based on Modularity
... - OpenMP versions of algorithms in [1] are available to download. - Main suite containing three community detection algorithms based on the Modularity measure containing: Geodesic and Random Walk edge Betweenness [1] and Spectral Modularity [2]. Collaborator: Theologos Kotsos. [1] M. Newman & M. Girvan, Physical Review, E 69 (026113), 2004. [2] M. Newman, Physical Review E, 74(3):036104, 2006. [3] B. Ball et al, An efficient and principled method for detecting communities in networks, 2011. The suite is based upon the fast community algorithm implemented by Aaron Clauset <aaron@cs.unm.edu>, Chris Moore, Mark Newman, and the R IGraph library Copyright (C) 2007 Gabor Csardi <csardi@rmki.kfki.hu>. ... -
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Graphlet kernel framework
Calculates similarity between neighborhoods of two vertices in a graph
This software package provides a framework for calculating similarity between neighborhoods rooted at two vertices of interest in a labeled graph (undirected or directed). The list of available similarity functions includes: cumulative random walk, standard random walk, standard graphlet kernel, edit distance graphlet kernel, label substitution graphlet kernel and edge indel graphlet kernel. The graphlet kernel framework can be used for vertex (node) classification in graphs, kernel-based clustering, or community detection. If you use this framework, please cite the following paper: Lugo-Martinez J, Radivojac P. ... -
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JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.
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Random Walk
A small Programm to show that even random has a direction.
Random Walk is a small programm I devoleped by myself. It shows that even when generating random numbers there is a tendency in on direction. I am no professional programmer, so please bear with me, when viewing the source code or when you are trying to give me tips. :-) There is still a lot left to be done. (Especially on creating random numbers and such). -
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GenePrioritization is a program to prioritize disease genes based on the random walk with restart algorithm for any kind of gene/protein networks. Putative genes can be verified through evidences found in PubMed, OMIM, and GeneRIF databases.
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GHIRL is the Graph-based Heterogeneous Information Representation Language: a java library for representing, querying, and navigating graph- or network-based data structures.
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