[英]Graph partition algo with Neo4j graph database
I know there has some famous graph partition algo tools like METIS which is implemented by karypis Lab ( http://glaros.dtc.umn.edu/gkhome/metis/metis/overview ) 我知道有一些着名的图形分区算法工具,如METIS,由karypis Lab实施( http://glaros.dtc.umn.edu/gkhome/metis/metis/overview )
but I wanna know is there any method to partition graph stored in Neo4j? 但我想知道是否有任何方法来分割存储在Neo4j中的图形? or I have to dump the Neo4j's data and transform the node and edge format manually to fit the METIS input format?
或者我必须转储Neo4j的数据并手动转换节点和边缘格式以适应METIS输入格式?
Regarding new-ish and interesting algorithms, this is by no means exhaustive or state of the art, but these are the first places I would look: 关于新的和有趣的算法,这绝不是详尽的或现有的,但这些是我看的第一个地方:
Specific Algorithm : DiDiC (Distributed Diffusive Clustering) - I used it once in my thesis ( Partitioning Graph Databases ) 特定算法 : DiDiC(分布式扩散聚类) - 我在论文中使用过一次( 分区图数据库 )
Specific Algorithm : EvoCut "Finding sparse cuts locally using evolving sets" - local probabilistic algorithm from Microsoft - related to these papers 特定算法 : EvoCut“使用演化集在本地查找稀疏剪切” - 来自Microsoft的本地概率算法 - 与这些论文相关
General Algorithm Family : Hierarchical Graph Clustering 通用算法族 : 分层图聚类
From a high level: 从高层次:
Notes: 笔记:
General limitations - the things few clustering algorithms do: 一般限制 - 几乎没有聚类算法的事情:
Having worked independently with METIS and Neo4j in the past, I am not aware of any tool for generating a METIS file from Neo4j. 在过去与METIS和Neo4j独立工作后,我不知道有任何工具可以从Neo4j生成METIS文件。 That being said, writing such a tool should be an easy task and would be a great community contribution.
话虽这么说,编写这样一个工具应该是一项容易的任务,并且将是一个很好的社区贡献。
Another approach for integrating METIS with Neo4j might be in connecting METIS to Neo4j from C++ via JNI. 将METIS与Neo4j集成的另一种方法可能是通过JNI将METIS从C ++连接到Neo4j。 However this is going to be much more involved as it would have to take care of things like transactions, concurrency etc.
然而,这将涉及更多,因为它必须处理交易,并发等事情。
On the more general question of partitioning graphs, it is quite possible to implement some of the more known and simple algorithms with reasonable effort. 关于划分图的更一般的问题,很有可能通过合理的努力实现一些更为人熟知和简单的算法。
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