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Pattern Mining in Multi-weighted Graphs
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ReSuM-UND
src/main/java
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ReadMe.md
config.cfg
generate_weights.py
paper.pdf
pom.xml
run.sh

ReadMe.md

ReSuM

Overview

ReSuM is a framework to mine relevant patterns from large weighted and multi-weighted graphs. Assuming that the importance of a pattern is determined not only by its frequency in the graph, but also by the edge weights of its appearances, we propose four scoring functions to compute the relevance of the patterns. These functions satisfy the apriori property, and thus can rely on efficient pruning strategies.

The framework includes an exact and an approximate mining algorithm. The first is characterized by intelligent storage and computation of the pattern scores, while the second is based on the aggregation of similar weighting functions to allow scalability and avoid redundant computations.

Content

run.sh ...........
config.cfg ...............
ReSuM/ ...................
generate_weights.py ......

Requirements

Java JRE v1.8.0

Input Format

Graph File

This file contains the information about the nodes and the edges in the graph, and must comply with the following format:

v <node_id> <node_label>
v <node_id> <node_label>
...
e <node_id1> <node_id2> <edge_label>
e <node_id1> <node_id2> <edge_label>
...

In particular, it must first list all the graph nodes in ascending order of id, and then all the edges. Lines corresponding to nodes start with the letter v, while those corresponding to edges start with the letter e. ReSuM currently supports only double values as labels, and integer values as node and edge ids.

The datasets we used in our experiments are available on Google Drive at this link.

Weight File

This file contains the sets of edge weights for the input graph. Each line corresponds to a set of weights, one per graph edge, and must be a space-separated list of double values. Notice that the value at position i in the list is treated as the weight of the edge at position i in the graph file.

The weight file can be generated using the script generate_weights.py included in this package. To use the script you need to run the following command:

python generate_weights.py <graph_file> <num_weight_sets> <output_file> -d <dispersion> -a <alpha> -b <beta>

This command generates numweightsets weights for each edge in graphfile, following a beta distribution with coefficients alpha and beta, and a dispersion parameter dispersion on the percentage of edges weighted for each edge label. The result is written in outputfile.

Usage

You can use ReSuM either by using the script run.sh included in this package or by running the following commands.

Using the Script

  1. Input: the name of the graph file must end with the extension .lg, while the name of the weight file must follow the pattern <graphfile><dispersion>.w, where dispersion indicates the percentage of graph edges weighted for each edge label. Both files must be in the same folder named with the same name of the graph (i.e. if the graph is called graph.lg, the folder must be called graph).

  2. Settings: the value of each parameter used by ReSuM must be set in the configuration file config.cfg. In particular:

  • General settings:
    • input_data: path to the folder that contains the graph file and the weight file.
    • output_data: path to the folder where the results will be saved.
    • tasks: space-separated list of the scoring functions to use to mine the weighted patterns (1 = ALL, 2 = ANY, 3 = SUM, 4 = AVG). The code will run once for each scoring function.
    • rels: space-separated list of relevance thresholds to use. The code will run once for each threshold value.
    • funcs: space-separated list of numbers of weight sets to test. The code will run once for each number of sets.
    • disp: dispersion value, as indicated in the name of the weight file.
    • multirun: number of runs for each configuration of the parameters.
    • maxSize: threshold on the size of the patterns to mine. A pattern will not be expanded if its size reaches this threshold.
    • weightFileSize: number of lines of the file containing the weights of the graph.
  • Dataset-related settings:
    • Dataset names: file names of the datasets to test.
    • Default values: comma-separated list of default values and information about the graph, i.e., default frequency threshold, default relevance threshold, is-directed, num of buckets to use in the clustering step, has-labels-on-the-nodes
    • Frequencies: comma-separated list of frequency thresholds to test
    • Experimental flags: test to perform among (1) test many frequency thresholds, (2) test many relevance thresholds, (3) test many scoring functions, (4) compute clustering, (5) run ReSuM-approximate.
  • ReSuM-approximate settings:
    • clustType: clustering strategy to use for the aggregation of the weighting functions. The values allowed are bucket and full, which correspond to the bucket-based and the full-vector strategy, respectively. This strategy is used to create the feature vectors of the weighting functions.
    • buck: number of buckets to use to create the feature vectors, when the clustering type is bucket.
    • smart: if true, the seed set used by the clustering algorithm is initialized by selecting the most diverse feature vectors. If false, the vectors in the seed set are chosen randomly.
  1. Declaration of the datasets: the arrays that store the names, the frequencies, and the experimental flags of each dataset to test must be declared at the beginning of the script run.sh. For example in the script run.sh you can write:

     declare -A datasets
     datasets[$amazon_db]=$amazon_defaults
     declare -A test_freqs
     test_freqs[$amazon_db]=$amazon_freqs
     declare -A flags
     flags[$amazon_db]=$amazon_flags
    

while in the configuration file config.cfg:

    amazon_db='amazon'
    amazon_defaults=130,0.0001,true,10,false
    amazon_freqs=190,180,140,133,120,115
    amazon_flags='0,0,1,0,0' 

Running the Commands

  1. Run ReSuM:

     java -cp ReSuM.jar:lib/* sa.edu.kaust.grami.dijkstra.Main caching=true automorphism=true inputFileName=<dataset_name> weightFileName=<weight_file> disp=<dispersion> freq=<frequency_threshold> rel=<relevance_threshold> multipleRuns=<num_runs> score=<scoring_function> numberOfEdgeWeights=<num_weights_per_edge> actualNumOfEdgeWeights=<num_weights_per_edge>  ignoreLabels=<true_if_graph_has_no_node_labels> maxSize=<max_size_of_patterns_to_mine> weightFileSize=<num_lines_of_weight_file> datasetsFolder=<input_folder> outputFolder=<output_folder>
    
  2. Run ReSuM-approximate:

     java -cp ReSuM.jar:lib/* eu.unitn.disi.db.resum.clustering.KGraphGen inputFileName=<dataset_name> weightFileName=<weight_file> buckets=<num_buckets> disp=<dispersion> clusteringType=<bucket_or_full> numberOfEdgeWeights=<num_weights_per_edge> actualNumOfEdgeWeights=<num_clusters> smart=<initialization_is_not_random> ignoreLabels=<true_if_graph_has_no_node_labels> maxSize=<max_size_of_patterns_to_mine> weightFileSize=<num_lines_of_weight_file> datasetsFolder=<input_folder> outputFolder=<output_folder>
     java -cp ReSuM.jar:lib/* sa.edu.kaust.grami.dijkstra.Main caching=true automorphism=true inputFileName=<dataset_name> weightFileName=<dataset_name>_<dispersion>_<num_clusters>.cw disp=<dispersion> freq=<frequency_threshold> rel=<relevance_threshold> score=<scoring_function> multipleRuns=<num_runs> numberOfEdgeWeights=<num_weights_per_edge> actualNumOfEdgeWeights=<num_clusters> clusteringType=<bucket_or_full> buckets=<num_buckets> ignoreLabels=<true_if_graph_has_no_node_labels> maxSize=<max_size_of_patterns_to_mine> weightFileSize=<num_lines_of_weight_file> datasetsFolder=<input_folder> outputFolder=<output_folder>
    

Note that if the graph is undirected, you must replace ReSuM.jar with ReSuM-UND.jar. Do not replace it in the cluster generation.

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