diff --git a/.Rbuildignore b/.Rbuildignore index 91114bf..e13c405 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,2 +1,3 @@ ^.*\.Rproj$ ^\.Rproj\.user$ +^LICENSE\.md$ diff --git a/DESCRIPTION b/DESCRIPTION new file mode 100644 index 0000000..ebceca5 --- /dev/null +++ b/DESCRIPTION @@ -0,0 +1,19 @@ +Package: influence.mining +Title: Provides various methods for influence mining on Graphs +Version: 0.2.0 +Authors@R: person(given = "Owais", + family = "Hussain", + role = c("aut", "cre"), + email = "owaishussain@outlook.com", + comment = c(ORCID = "0000-0002-5449-1867", Researchgate = "https://www.researchgate.net/profile/Owais_Hussain")) +Description: This package uses igraph library to provide influence mining + features on graph data +Depends: + R (>= 3.1.0) +License: GPL-3 +Encoding: UTF-8 +LazyData: true +Roxygen: list(markdown = TRUE) +RoxygenNote: 7.1.0 +Suggests: + testthat diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 42abb9b..0000000 --- a/LICENSE +++ /dev/null @@ -1,340 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 2, June 1991 - - Copyright (C) 1989, 1991 Free Software Foundation, Inc., - 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The licenses for most software are designed to take away your -freedom to share and change it. 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It is safest -to attach them to the start of each source file to most effectively -convey the exclusion of warranty; and each file should have at least -the "copyright" line and a pointer to where the full notice is found. - - influence-mining - Copyright (C) {year} Owais A. Hussain - - This program is free software; you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation; either version 2 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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Of course, the commands you use may -be called something other than `show w' and `show c'; they could even be -mouse-clicks or menu items--whatever suits your program. - -You should also get your employer (if you work as a programmer) or your -school, if any, to sign a "copyright disclaimer" for the program, if -necessary. Here is a sample; alter the names: - - I, Owais A. Hussain hereby disclaims all copyright interest in the program - `influence-mining' written by me. - - Owais A. Hussain, 13-Sep-2015 - Author - -This General Public License does not permit incorporating your program into -proprietary programs. If your program is a subroutine library, you may -consider it more useful to permit linking proprietary applications with the -library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. - diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..d7ad3d0 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,601 @@ +GNU General Public License +========================== + +_Version 3, 29 June 2007_ +_Copyright © 2007 Free Software Foundation, Inc. <>_ + +Everyone is permitted to copy and distribute verbatim copies of this license +document, but changing it is not allowed. + +## Preamble + +The GNU General Public License is a free, copyleft license for software and other +kinds of works. + +The licenses for most software and other practical works are designed to take away +your freedom to share and change the works. By contrast, the GNU General Public +License is intended to guarantee your freedom to share and change all versions of a +program--to make sure it remains free software for all its users. 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No Surrender of Others' Freedom + +If conditions are imposed on you (whether by court order, agreement or otherwise) +that contradict the conditions of this License, they do not excuse you from the +conditions of this License. If you cannot convey a covered work so as to satisfy +simultaneously your obligations under this License and any other pertinent +obligations, then as a consequence you may not convey it at all. For example, if you +agree to terms that obligate you to collect a royalty for further conveying from +those to whom you convey the Program, the only way you could satisfy both those terms +and this License would be to refrain entirely from conveying the Program. + +### 13. Use with the GNU Affero General Public License + +Notwithstanding any other provision of this License, you have permission to link or +combine any covered work with a work licensed under version 3 of the GNU Affero +General Public License into a single combined work, and to convey the resulting work. +The terms of this License will continue to apply to the part which is the covered +work, but the special requirements of the GNU Affero General Public License, section +13, concerning interaction through a network will apply to the combination as such. + +### 14. Revised Versions of this License + +The Free Software Foundation may publish revised and/or new versions of the GNU +General Public License from time to time. Such new versions will be similar in spirit +to the present version, but may differ in detail to address new problems or concerns. + +Each version is given a distinguishing version number. If the Program specifies that +a certain numbered version of the GNU General Public License “or any later +version” applies to it, you have the option of following the terms and +conditions either of that numbered version or of any later version published by the +Free Software Foundation. If the Program does not specify a version number of the GNU +General Public License, you may choose any version ever published by the Free +Software Foundation. + +If the Program specifies that a proxy can decide which future versions of the GNU +General Public License can be used, that proxy's public statement of acceptance of a +version permanently authorizes you to choose that version for the Program. + +Later license versions may give you additional or different permissions. However, no +additional obligations are imposed on any author or copyright holder as a result of +your choosing to follow a later version. + +### 15. Disclaimer of Warranty + +THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. +EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES +PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER +EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE +QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE +DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + +### 16. Limitation of Liability + +IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY +COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS +PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, +INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE +PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE +OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE +WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGES. + +### 17. Interpretation of Sections 15 and 16 + +If the disclaimer of warranty and limitation of liability provided above cannot be +given local legal effect according to their terms, reviewing courts shall apply local +law that most closely approximates an absolute waiver of all civil liability in +connection with the Program, unless a warranty or assumption of liability accompanies +a copy of the Program in return for a fee. + +_END OF TERMS AND CONDITIONS_ + +## How to Apply These Terms to Your New Programs + +If you develop a new program, and you want it to be of the greatest possible use to +the public, the best way to achieve this is to make it free software which everyone +can redistribute and change under these terms. + +To do so, attach the following notices to the program. It is safest to attach them +to the start of each source file to most effectively state the exclusion of warranty; +and each file should have at least the “copyright” line and a pointer to +where the full notice is found. + + influence.mining is a package for influence mining on graph data + Copyright (C) 2020 GPLv3 + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + +If the program does terminal interaction, make it output a short notice like this +when it starts in an interactive mode: + + influence.mining Copyright (C) 2020 GPLv3 + This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type 'show c' for details. + + I, Owais A. Hussain hereby disclaims all copyright interest in the program + influence.mining written by me. + Owais A. Hussain, 24-Jun-2020 + Author + + +The hypothetical commands `show w` and `show c` should show the appropriate parts of +the General Public License. Of course, your program's commands might be different; +for a GUI interface, you would use an “about box”. + +You should also get your employer (if you work as a programmer) or school, if any, to +sign a “copyright disclaimer” for the program, if necessary. For more +information on this, and how to apply and follow the GNU GPL, see +<>. + +The GNU General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may consider it +more useful to permit linking proprietary applications with the library. If this is +what you want to do, use the GNU Lesser General Public License instead of this +License. But first, please read +<>. diff --git a/influence-mining.Rproj b/influence.mining.Rproj similarity index 87% rename from influence-mining.Rproj rename to influence.mining.Rproj index 0b39452..f30c4ec 100644 --- a/influence-mining.Rproj +++ b/influence.mining.Rproj @@ -15,5 +15,7 @@ LaTeX: pdfLaTeX StripTrailingWhitespace: Yes BuildType: Package +PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source +PackageCheckArgs: --as-cran PackageRoxygenize: rd,collate,namespace diff --git a/man/clique.community.Rd b/man/clique.community.Rd deleted file mode 100644 index 50e52e7..0000000 --- a/man/clique.community.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{clique.community} -\alias{clique.community} -\title{This function detects communities using cliques} -\usage{ -clique.community(graph, k) -} -\arguments{ -\item{graph}{is igraph object} - -\item{k}{is the number of communitites to find} -} -\description{ -This function detects communities using cliques -} -\references{ -Palla, Gergely, et al. "Uncovering the overlapping community structure of complex networks in nature and society." Nature 435.7043 (2005): 814-818. -} - diff --git a/man/community.significance.test.Rd b/man/community.significance.test.Rd deleted file mode 100644 index a5274ce..0000000 --- a/man/community.significance.test.Rd +++ /dev/null @@ -1,12 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{community.significance.test} -\alias{community.significance.test} -\title{This function performs a Wilcoxon rank-sum test on the "internal" and "external" degrees of a community in order to quantify its significance.} -\usage{ -community.significance.test(graph, vs, ...) -} -\description{ -The edges within a community are "internal" and the edges connecting the vertices of a community with the rest of the graph are "external". More internal than external edges show that the community is significant; the otherwise suggests that the community is in fact an "anti-community". -} - diff --git a/man/community_influence.Rd b/man/community_influence.Rd deleted file mode 100644 index 5df2416..0000000 --- a/man/community_influence.Rd +++ /dev/null @@ -1,12 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{community_influence} -\alias{community_influence} -\title{This function finds influential nodes in communities in given graph} -\usage{ -community_influence() -} -\description{ -This function finds influential nodes in communities in given graph -} - diff --git a/man/find_communities.Rd b/man/find_communities.Rd deleted file mode 100644 index 2e1ddca..0000000 --- a/man/find_communities.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{find_communities} -\alias{find_communities} -\title{This method finds communities in the given graph and returns the graph after adding a vector "group" to its vertices} -\usage{ -find_communities(graph, plot = TRUE, method = c("multilevel", - "edgebetweenness", "fastgreedy", "eigenvector", "spinglass", "walktrap", - "labelpropagation", "clique", "largescale")) -} -\arguments{ -\item{graph}{is the igraph object} - -\item{plot}{whether to plot the graph using tkplot. Default is TRUE} - -\item{method}{is the method to find communities. Value can be "multilevel", "edgebetweenness", "fastgreedy", "eigenvector", "spinglass", "walktrap", "labelpropagation", "clique", "largescale"} - -\item{prob}{is the probability of activation of a neighbour node. This is applicable only to IC model currently} -} -\description{ -This method finds communities in the given graph and returns the graph after adding a vector "group" to its vertices -} - diff --git a/man/graph_summary.Rd b/man/graph_summary.Rd deleted file mode 100644 index 7699e86..0000000 --- a/man/graph_summary.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{graph_summary} -\alias{graph_summary} -\title{This functions gives a summary of common metrics of given graph} -\usage{ -graph_summary(graph, plot = FALSE) -} -\arguments{ -\item{graph}{is the igraph object} - -\item{plot}{uses tkplot to plot the graph. Default is FALSE} -} -\value{ -object containing summary -} -\description{ -This functions gives a summary of common metrics of given graph -} - diff --git a/man/influence.Rd b/man/influence.Rd deleted file mode 100644 index 9e0a2aa..0000000 --- a/man/influence.Rd +++ /dev/null @@ -1,40 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{influence} -\alias{influence} -\title{This function is a wrapper for influence_IC and influence_LT functions} -\usage{ -influence(graph, seed = NULL, budget = 1, steps = 1, model = c("IC", - "LT"), maximize = FALSE, seed_method = c("random", "degree", "closeness", - "betweenness", "coreness", "eigenvector", "a-degree", "a-closeness", - "a-betweenness", "a-coreness", "a-eigenvector"), prob = 0.5) -} -\arguments{ -\item{graph}{is the igraph object} - -\item{seed}{(optional) is a set of seed (initial nodes). If NULL, then seed_method parameter should be given} - -\item{budget}{defines what percentage of most influential nodes out of all nodes is required as output. Default value is 1} - -\item{steps}{is the time steps for which, the diffusion process should run. If exhaustive run is required, provide a high value (like 100). Default value is 1} - -\item{model}{is influence model to run the dataset on. Value MUST either be "LT" or "IC"} - -\item{maximize}{should be TRUE if influential nodes are to be derived using Greedy algorithm} - -\item{seed_method}{is the selection method for seed (initial nodes). Value can be "random", "degree", "closeness", "betweenness", "coreness", "eigenvector", "a-degree", "a-closeness", "a-betweenness", "a-coreness", "a-eigenvector"} - -\item{prob}{is the probability of activation of a neighbour node. This is applicable only to IC model currently} -} -\value{ -output containing summary -} -\description{ -This function is a wrapper for influence_IC and influence_LT functions -} -\examples{ -influence(G, budget=5, seed=NULL, 5, "LT", maximize=TRUE, seed_method="degree", prob=0.5) -influence(G, budget=5, seed=NULL, 5, "IC", maximize=TRUE, seed_method="degree", prob=0.5) -influence(G, budget=5, seed=c(2,5,9,23), 5, "IC", maximize=FALSE, prob=0.5) -} - diff --git a/man/influence_IC.Rd b/man/influence_IC.Rd deleted file mode 100644 index ffaa272..0000000 --- a/man/influence_IC.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{influence_IC} -\alias{influence_IC} -\usage{ -influence_IC(graph, seed, steps, prob) -} -\arguments{ -\item{graph}{is the igraph object} - -\item{seed}{is the initial seed nodes passed} - -\item{steps}{is the time steps for which, the diffusion process should run. If exhaustive run is required, provide a high value (like 100). Default value is 1} - -\item{prob}{is the probability of activation of a neighbour node. This is applicable only to IC model currently} -} -\value{ -output containing summary -} - diff --git a/man/influence_LT.Rd b/man/influence_LT.Rd deleted file mode 100644 index b45b031..0000000 --- a/man/influence_LT.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{influence_LT} -\alias{influence_LT} -\usage{ -influence_LT(graph, seed, steps, threshold) -} -\arguments{ -\item{graph}{is the igraph object} - -\item{seed}{is the initial seed nodes passed} - -\item{steps}{is the time steps for which, the diffusion process should run. If exhaustive run is required, provide a high value (like 100). Default value is 1} - -\item{threshold}{is minimum threshold required to activate a node under observation} -} -\value{ -output containing summary -} - diff --git a/man/influence_max.Rd b/man/influence_max.Rd deleted file mode 100644 index 835761d..0000000 --- a/man/influence_max.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{influence_max_greedy_greedy} -\alias{influence_max_greedy} -\title{This function implements Greedy algorithm for Influence Maximization} -\usage{ -influence_max_greedy(graph, budget, steps, model, prob) -} -\arguments{ -\item{graph}{is the igraph object} - -\item{budget}{defines what percentage of most influential nodes out of all nodes is required as output. Default value is 1} - -\item{steps}{is the time steps for which, the diffusion process should run. If exhaustive run is required, provide a high value (like 100). Default value is 1} - -\item{model}{is influence model to run the dataset on. Value MUST either be "LT" or "IC"} - -\item{prob}{is the probability of activation of a neighbour node. This is applicable only to IC model currently} -} -\value{ -output containing summary -} -\description{ -This function implements Greedy algorithm for Influence Maximization -} -\examples{ -influence_max_greedy(graph, budget=2, steps=5, "LT", prob=0.5) -influence_max_greedy(graph, budget=5, steps=99, "IC", prob=0.5) -} diff --git a/man/large.scale.community.Rd b/man/large.scale.community.Rd deleted file mode 100644 index 37e5744..0000000 --- a/man/large.scale.community.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{large.scale.community} -\alias{large.scale.community} -\title{This function detects communities in large-scale graphs} -\usage{ -large.scale.community(graph, mode = "all") -} -\arguments{ -\item{graph}{is igraph object} - -\item{mode}{is detection mode. Values can be "all", "in" or "out". Default is "all"} -} -\description{ -This function detects communities in large-scale graphs -} -\references{ -Raghavan, Usha Nandini, Réka Albert, and Soundar Kumara. "Near linear time algorithm to detect community structures in large-scale networks." Physical Review E 76.3 (2007): 036106. -} - diff --git a/man/largest_component.Rd b/man/largest_component.Rd deleted file mode 100644 index 7d8aaca..0000000 --- a/man/largest_component.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{largest_component} -\alias{largest_component} -\title{This function returns largest connected component in a network} -\usage{ -largest_component(graph) -} -\arguments{ -\item{graph}{is the igraph object} -} -\value{ -largest component igraph object -} -\description{ -This function returns largest connected component in a network -} - diff --git a/man/new_influence_max.Rd b/man/new_influence_max.Rd deleted file mode 100644 index 11f2988..0000000 --- a/man/new_influence_max.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{new_influence_max_greedy} -\alias{new_influence_max_greedy} -\title{Try to improve maximization. NOT TESTED} -\usage{ -new_influence_max_greedy(graph, budget, steps, model, prob) -} -\arguments{ -\item{graph}{is the igraph object} - -\item{budget}{defines what percentage of most influential nodes out of all nodes is required as output. Default value is 1} - -\item{steps}{is the time steps for which, the diffusion process should run. If exhaustive run is required, provide a high value (like 100). Default value is 1} - -\item{model}{is influence model to run the dataset on. Value MUST either be "LT" or "IC"} - -\item{prob}{is the probability of activation of a neighbour node. This is applicable only to IC model currently} -} -\value{ -output summary -} -\description{ -Try to improve maximization. NOT TESTED -} - diff --git a/man/select_seed.Rd b/man/select_seed.Rd deleted file mode 100644 index 2b3d61e..0000000 --- a/man/select_seed.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Influence.R -\name{select_seed} -\alias{select_seed} -\title{This function inputs a graph object, percentage and a seed method and returns k% nodes as seed using given method} -\usage{ -select_seed(graph, budget, seed_method = c("random", "degree", "closeness", - "betweenness", "coreness", "eigenvector", "a-degree", "a-closeness", - "a-betweenness", "a-coreness", "a-eigenvector")) -} -\arguments{ -\item{graph}{is the igraph object} - -\item{budget}{defines what percentage of most influential nodes out of all nodes is required as output. Default value is 1} - -\item{seed_method}{is the selection method for seed (initial nodes). Value can be "random", "degree", "closeness", "betweenness", "coreness", "eigenvector", "a-degree", "a-closeness", "a-betweenness", "a-coreness", "a-eigenvector"} -} -\value{ -set of nodes -} -\description{ -This function inputs a graph object, percentage and a seed method and returns k% nodes as seed using given method -} - diff --git a/util/graph_util.R b/util/graph_util.R index 9b6e41d..fb15210 100644 --- a/util/graph_util.R +++ b/util/graph_util.R @@ -1,8 +1,11 @@ #' This functions gives a summary of common metrics of given graph +#' #' @name graph_summary #' @param graph is the igraph object -#' @param plot uses tkplot to plot the graph. Default is FALSE +#' @param plot uses tkplot to plot the \code{graph}. Default is FALSE #' @return object containing summary +#' @examples +#' graph_summary(graph) graph_summary <- function(graph, plot=FALSE) { o <- NULL degrees <- degree(graph) @@ -29,6 +32,7 @@ graph_summary <- function(graph, plot=FALSE) { } #' Calculates several traits from given graph and returns as data frame +#' #' @name get_graph_traits #' @param graph is the igraph object #' @param normalize uses pnorm function to normalize the traits. Default is FALSE @@ -108,6 +112,7 @@ get_graph_traits <- function(graph, normalize=FALSE, } #' Calculates several node influence traits from given graph and returns as a list +#' #' @name get_node_influence_traits #' @param graph is the igraph object #' @param normalize uses pnorm function to normalize the traits. Default is FALSE @@ -227,14 +232,21 @@ get_node_influence_traits <- function(graph, normalize=FALSE, traits=c("betweenn data } +#' This function normalizes the numeric values passed in \code{x} between 0 and 1 +#' +#' @name normalize_trait +#' @param x is data to be normalized +#' @return normalized \code{x} normalize_trait <- function(x) { pnorm(x, mean(x), sd(x)) } #' This function can be used to normalize a set of numeric variables in a dataset between 0 and 1. Non-numeric data will be skipped +#' #' @name normalize_data #' @param data is data frame to be normalized #' @param columns is list of columns to be normalized +#' @return normalized data frame normalize_data <- function(data, columns) { for (column in columns) { # Skip non-numeric data @@ -249,6 +261,7 @@ normalize_data <- function(data, columns) { } #' This function plots degree distribution of given graph +#' #' @name plot_degree_distribution #' @param graph is the igraph object plot_degree_distribution <- function(graph) { @@ -265,6 +278,7 @@ plot_degree_distribution <- function(graph) { } #' This function plots degree distribution and returns power-law exponent of given graph +#' #' @name fit_power_law #' @param graph is the igraph object fit_power_law = function(graph) { @@ -282,6 +296,7 @@ fit_power_law = function(graph) { } #' This function generates a tree-structured graph +#' #' @name generate_tree #' @param size is the number of nodes #' @param children is the number of children each node has (in addition to a parent node) @@ -293,6 +308,7 @@ generate_tree <- function(size, children=2, direction='undirected') { } #' This function generates a ring-structured graph, in which nodes are connected with neighbours within given distance +#' #' @name generate_ring #' @param size is the number of nodes #' @param distance defines maximum distance each node has to its farthest direct neighbour @@ -303,6 +319,7 @@ generate_ring <- function(size, distance) { } #' This function generates a fully connected undirected graph +#' #' @name generate_clique #' @param size is the number of nodes #' @return igraph object @@ -312,6 +329,7 @@ generate_clique <- function(size) { } #' This function generates a Erdos Renyi random graph +#' #' @name generate_random #' @param size is the number of nodes #' @param probability is the probability of edge formation between nodes @@ -324,6 +342,7 @@ generate_random <- function(size, probability=0.2, directed=FALSE, allow_cycles= } #' This function generates a Watts & Strogatz small-world graph by rewiring a random graph, while keeping the degree distribution consistent +#' #' @name generate_small_world #' @param size is the number of nodes #' @param probability is the probability of edge formation between nodes @@ -338,6 +357,7 @@ generate_small_world <- function(size, probability=0.1, directed=FALSE, allow_cy } #' This function generates a Barabasi scale-free graph +#' #' @name generate_scale_free #' @param size is the number of nodes #' @param preference is the power of preferencial attachment. Default is linear, i.e. 1 @@ -350,7 +370,7 @@ generate_scale_free <- function(size, preference=1, directed=FALSE, allow_cycles } #' Holme-Kim Network -#' +#' #' @description Simulate a scale-free network with relatively high clustering, comparing to B-A networks (Holme and Kim, 1999). #' @param size is the number of nodes of the network #' @param m is the number of nodes to which a new node connects at each iteration @@ -404,6 +424,7 @@ net.holme.kim <- function( n, m, pt ){ } #' This function returns largest connected component in a network +#' #' @name largest_component #' @param graph is the igraph object #' @return largest component igraph object diff --git a/util/influence_maximization.R b/util/influence_maximization.R index e780ae7..f63d33f 100644 --- a/util/influence_maximization.R +++ b/util/influence_maximization.R @@ -383,7 +383,6 @@ ic_spread <- function (graph, seed, runs=100) { } count <- count + simulate_ic(graph, active); total <- total + count; - #print(paste('Spread for run #', i, count)) } round(total / runs, 5) } @@ -449,20 +448,14 @@ simulate_ic <- function(graph, active) { neighbour_nodes <- neighbors(graph, node) # Remove already activated nodes from neighbours neighbour_nodes <- neighbour_nodes[!neighbour_nodes %in% active] + neighbour_nodes <- neighbour_nodes[!neighbour_nodes %in% tried] # Try to activate inactive neighbours according to the weight on edge for (j in 1:length(neighbour_nodes)) { - print("I'm here") - if (neighbour_nodes[j]$tried) { - next - } - print("Now here") weight <- E(graph, P=c(node, neighbour_nodes[j]))$weight - print("Still alive") if (runif(1) <= weight) { count <- count + 1 } - neighbour_nodes[j]$tried <- TRUE - print("Phew! Next") + tried <- c(tried, neighbour_nodes[j]) } } count @@ -615,7 +608,7 @@ get_influential_nodes <- function(graph, budget, measure="RESILIENCE", parallel= #' @param measure specifies the method to measure influence. Value MUST be "RESILIENCE", "INFLUENCE_IC" or "INFLUENCE_LT". Default is "RESILIENCE" #' @param parallel flag defines whether the execution will use parallel processing or not. Default is FALSE #' @return vector of resiliences of provided graph -get_influential_nodes_greedy <- function(graph, budget, method="RESILIENCE") { +get_influential_nodes_greedy <- function(graph, budget, measure="RESILIENCE") { size <- length(V(graph)) if (budget < 1) { budget <- budget * size @@ -630,10 +623,10 @@ get_influential_nodes_greedy <- function(graph, budget, method="RESILIENCE") { output <- NULL # For all nodes except seed for (node in setdiff(nodes, top_nodes)) { - # Find resilience of node with existing nodes in seed - output <- resilience(graph, c(top_nodes, node)) + # Find influence of node with existing nodes in seed + output <- get_influence(graph, c(top_nodes, node), measure=measure) # If current node causes more influence than maximum so far, then swap - if (output < max_influence) { + if ((measure == "RESILIENCE" && output < max_influence) || (measure != "RESILIENCE" && output > max_influence)) { most_influential <- node max_influence <- output }