# Differences

This shows you the differences between two versions of the page.

public:osn_software [2015/08/12 16:35] minas created |
public:osn_software [2016/05/24 18:03] minas |
||
---|---|---|---|

Line 1: | Line 1: | ||

===== Software released by Online Social Networks project===== | ===== Software released by Online Social Networks project===== | ||

+ | * 2K Simple: We release a reference implementation (in both Python and C++) of the graph construction algorithm 2K_Simple that was presented in our paper [[http://minasgjoka.com/papers/mgjoka_infocom15.pdf|Construction of Simple Graphs with a Target Joint Degree Matrix and Beyond]]. 2K_Simple receives as input a Joint Degree Matrix (JDM) and provably constructs a simple graph with the given JDM in running time O(|E|*d_max) where |E| is number of edges and d_max is the maximum degree as defined by the given JDM. Finally, we have [[http://networkx.readthedocs.io/en/latest/reference/generated/networkx.generators.joint_degree_seq.joint_degree_graph.html|integrated the 2K_Simple algorithm in the NetworkX Python]] package (version >=2.0). | ||

* [[http://www.minasgjoka.com/cliques/instructions/index.html|Clique Estimation]]: two Python scripts that demonstrate the estimators described in our paper [[http://www.minasgjoka.com/papers/gjoka13_CliqueEstimation.pdf|Estimating Clique Composition and Size Distributions from Sampled Network Data]]. The first script implements two types of unbiased estimators of clique size distributions, one of which exploits labeling of sampled nodes neighbors and one of which does not require this information. Additionally, it supports the compositions of cliques by node attributes (only supports binary node attributes, such as gender). The second script demonstrates how to prepare the data for input to the first script. More specifically, it receives as input a known graph, sampling parameters (sampling method, sampling size, replacement type), and clique distribution preferences (labeling, attributes). It then appropriately samples egonets from the given graph and calculates the maximal clique distribution for each sampled egonet. \\ | * [[http://www.minasgjoka.com/cliques/instructions/index.html|Clique Estimation]]: two Python scripts that demonstrate the estimators described in our paper [[http://www.minasgjoka.com/papers/gjoka13_CliqueEstimation.pdf|Estimating Clique Composition and Size Distributions from Sampled Network Data]]. The first script implements two types of unbiased estimators of clique size distributions, one of which exploits labeling of sampled nodes neighbors and one of which does not require this information. Additionally, it supports the compositions of cliques by node attributes (only supports binary node attributes, such as gender). The second script demonstrates how to prepare the data for input to the first script. More specifically, it receives as input a known graph, sampling parameters (sampling method, sampling size, replacement type), and clique distribution preferences (labeling, attributes). It then appropriately samples egonets from the given graph and calculates the maximal clique distribution for each sampled egonet. \\ |