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A Java Library Of Graph Algorithms And Optimization By Hang T Lau Torrent

There should be no significant performance reduction for using STINGER when compared with another general data structure across a broad set of typical graph algorithms. The data structure should allow parallel algorithms to exploit concurrency where possible. If you check it out, I'd be interested in your comments. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 6 years, 10 months ago. Active 5 years, 7 months ago.

Viewed 6k times. Load-balancing and Dynamic graph re -partitioning If you are interested in load-balancing and dynamic graph partitioning, you have several more options. Graph If you are working on the bleeding edge of concurrent search, optimization single source shortest path , and edge-oriented maximal independent set , you will also be interested in the freely available Graph benchmark. Aron Ahmadia Aron Ahmadia 5, 4 4 gold badges 26 26 silver badges 51 51 bronze badges. You are going to have to get deeper into the details of your algorithm for a better answer and it would probably be a new question.

Daniel Eberts Daniel Eberts 1 1 silver badge 5 5 bronze badges.


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But perhaps you can use it to declare shared data structures for your problem and run it's algorithms in different threads. Maybe you can subdivide your problem into suitable subproblems. He's well-known in the HPC community for his work on the Graph, thanks for mentioning this one!

Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. We repeat this process for each node until one pass through all nodes yields no community assignment changes. The second stage in the Louvain method uses the communities that were discovered in the community reassignment stage, to define a new coarse-grained network. In this network, the newly discovered communities are the nodes.

The relationship weight between the nodes representing two communities is the sum of the relationship weights between the lower-level nodes of each community. The rest of the Louvain method consists of the repeated application of stages 1 and 2. By applying stage 1 the community reassignment phase to the coarse-grained graph, we find a second tier of communities of communities of nodes. Then, in the next application of stage 2, we define a new coarse-grained graph at this higher-level of the hierarchy. We keep going like this until an application of stage 1 yields no reassignments.

At that point, repeated application of stages 1 and 2 will not yield any more modularity-optimizing changes, so the process is complete. Although the Louvain method, and modularity optimization algorithms more generally, have found wide application across many domains, some problems with these algorithms have been identified:. The following will run the algorithm and stream results:. The following will run the algorithm and write back results:. Charles is the only one who has friends in both communities, but he has more in community 4 so he fits better in that one.

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On the first level it found three communities with Alice, Bridget and Michael forming the first community, Charles, Doug and Mark forming the second one and Karin and Amy forming the third one. On the second level it found two communities. Alice, Bridget and Michael stay in the same community, but the other two communities merge into a single one. This sample will explain the pre-defined communities option of the Louvain algorithm.


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  8. Initial or pre-defined communities for nodes are specified using the communityProperty parameter. In this example the pre-defined communities are read from the community property, and the communities computed by the algorithm are stored to the newCommunity property. The default label and relationship-type projection has a limitation of 2 billion nodes and 2 billion relationships.

    (PDF) A Free Educational Java Framework for Graph Algorithms | Claudia D'Ambrosio - uxudoburubic.tk

    Therefore, if our projected graph contains more than 2 billion nodes or relationships, we will need to use huge graph projection. Set graph:'huge' in the config:. If label and relationship-type are not selective enough to describe your subgraph to run the algorithm on, you can use Cypher statements to load or project subsets of your graph.

    This can also be used to run algorithms on a virtual graph.

    Set graph:'cypher' in the config:. The property name that contains weight.


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    If null, treats the graph as unweighted. Must be numeric. The property name written back to the ID of the intermediate communities that particular node belongs to. Use 'heavy' when describing the subset of the graph with label and relationship-type parameter. Use 'cypher' for describing the subset with cypher node-statement and relationship-statement.

    The following returns the memory requirements for running Louvain on the Heavy Graph:. We can also get a break down of where that memory is assigned from the mapView field. The following returns the memory break down for running Louvain on the Heavy Graph:. The following returns the memory break down for running Louvain on the Huge Graph:. This dataset contains people and friends relationships from Pokec , the most popular online social network in Slovakia. This dataset contains Wikipedia pages and the links between them.

    Graphs in Java

    This dataset contains people and friends relationships from Friendster , the online gaming network. The Louvain algorithm. This section describes the Louvain algorithm in the Neo4j Graph Algorithms library. History and explanation Use-cases - when to use the Louvain algorithm Constraints - when not to use the Louvain algorithm Louvain algorithm sample Hierarchical Louvain algorithm sample Pre-defined communities Huge graph projection Cypher projection Syntax Graph type support Memory Requirements. History and explanation. Use-cases - when to use the Louvain algorithm.

    The Louvain method has been proposed to provide recommendations for Reddit users to find similar subreddits, based on the general user behavior. Find more details, see "Subreddit Recommendations within Reddit Communities". The Louvain method has been used to extract topics from online social platforms, such as Twitter and Youtube, based on the co-occurence graph of terms in documents, as a part of Topic Modeling process. Constraints - when not to use the Louvain algorithm. This is a general problem with modularity optimization algorithms; they have trouble detecting small communities in large networks.

    The degeneracy problem There is typically an exponentially large in network size number of community assignments with modularities close to the maximum. Research undertaken at Universite Catholique de Louvain showed that the different locally optimal community assignments can have quite different structural properties. For more information, see "The performance of modularity maximization in practical contexts".

    Louvain algorithm sample. Hierarchical Louvain algorithm sample. Pre-defined communities. Huge graph projection. Cypher projection.