I have a JUNG graph containing about 10K vertices and 100K edges, and I'd like to get a measure of similarity between any pair of vertices.
The vertices represent concepts (e.g. dog, house, etc), and the links represent relations between concepts (e.g. related, is_a, is_part_of, etc).
The vertices are densely inter-linked, so a shortest-path approach doesn't give good results (the shortest paths are always very short).
What approaches would you recommend to rank the connectivity between vertices?
JUNG has some algorithms to score the importance of vertices, but I don't understand if there are measures of similarity between 2 vertices.
SimPack seems also promising.
Any hints?
The centrality scores don't measure similarity of pairs of vertices, but some kind of (depending on the method) centrality of single nodes of the network in general. Therefore this approach is possibly not what you want.
SimPack indeed has a nice goal set out, but for graphs it implements isomorphism-based comparations, which rather compare multiple graphs for similarity than pairs of nodes of one given graph. Therefore this is out of scope for now.
What you are seeking are so-called graph clustering methods (also called network module determination or network community determination methods), which divide the graph (network) into multiple partitions so that the nodes in each partition are more strongly interconnected with each other than with nodes of other partitions.
The most classic method is maybe the betweenness centrality clustering of Newman & Girvan where you can exploit the dendrogram for similarity calculation, and it is in JUNG. Of course there are throngs of methods nowadays. You may want to try (shameless plug) our ModuLand method, or read the fine table of module detection algorithms at the end of the Electronic Supplementary Material. That is an overlapping graph clustering method family, that is its result for each node is a vector containing the strengths of belonging to any respective cluster of the network. Pairwise node similarity is easy to derive from pairs of these node-to-cluster vectors.
Graph clustering is non-trivial, and possible you would need to adapt any method for very precise domain-specific results, but that's up to the reader ;) Good luck!
Related
I need to cluster (let's say given as parameter k), words (that I
store in array List) according to their cosine similarity. I have stored my all words as vertexes in list in a complete ,weighed, and undirected graph (that uses adjacency list), and put their cosine similarity values on edges. As I understand I need to use MST (Kruskals Algorithm) for clustering process.
However since, my graph is complete graph and MST used for connected graphs, I am kind of confused how to use it on complete graph? Or I am doing wrong by using complete graph?
This is my wordList:
[directors, producers, film, movie, black, white, man, woman, person, man, young, woman, science, fiction, thrilling, realistic, lovely, stunning, criminals, zombies, father, son, girlfriend, boyfriend, nurse, soldier, professor, college]
And I need to cluster them by MST so that if k (number of clusters) is 2 it will be like this (2 clusters according to their similarities):
boyfriend,college,father,girlfriend,man,nurse,person,professor,son,woman,young
criminals,directors,fiction,film,lovely,movie,producers,science,stunning,thrilling,zombies
It's standard to use minimum spanning trees on complete graphs.
You will often find the runtime complexity given for this case separately. You may want to check if Prim's is faster than Kruskal's on a complete graph.
Clustering with the minimum spanning tree is also known as Single-Link clustering, and the fast SLINK algorithm is closely related to Prim's MST algorithm. But the output format is more suitable for clustering.
Attributed Graphs are most commonly represented as an adjacency matrix or a list where nodes are considered first class citizens. There are many graph queries such as neighborhood, shortest path, page rank, connected component that operate on these matrix and list structures on nodes. The attributes of the node/edge can also be stored apart from the connections.
Another representation of the graph is an incidence matrix where the incident edges of a node are recorded in a matrix. I understand they represent exactly the same information as previous node-based methods.
My question is, are there any graph queries/workloads/algorithms that can benefit from the incidence matrix structure rather than using the node-based structures i.e. favoring an edge-based structure? When exactly are the incidence matrix used?
I can think of only one case where incidence matrix may prove faster:
Finding the degree of a node or finding adjacent nodes is an operation with complexity O(V) when using an adjacency matrix and O(E) when using an incidence matrix.
Usually E>V, but this may not be the case if the graph has many 0-degree nodes. Since finding adjacent nodes is a basic operation, many algorithms may prove to be faster on such graphs.
I have a graph G = (V,E), with V the set of nodes, and E the set of edges. I have two types of nodes: Source nodes, and Consumer nodes (the number of Source nodes are way lower than the Consumer nodes). The nodes have geographic positions.
I want to partition the graph into a collection of sub-graphs which are:
a- connected sub-graphs,
b- of a proper size (the size of the partitions must be balanced; however not necessarily equal. e.g. between 2000-3000 nodes),
c- the partitions should preferably be directly connected to a Source. So if there is no Source in a partition, the path between the partition to a Source node should not include any nodes in the other partitions. (The most important constraint)
d- The nodes in a partition should be close to each other (geographically)
The minimum cut set is preferable. The Source nodes can be isolated from the other partitions (can be in partitions of one; only themselves).
Is there any existing partitioning technique that I can use? Any kind of help is fully appreciated.
There are some works based on the modularity measure used in community detection. For instance, in Chen et al. 2012, they extend the modularity to spatial, weighted, directed networks. The spatial distance is used to modulate the link weights.
This would fit your points a) and d). However, the (regular) modularity is not designed to find communities of similar size, so it won't fulfil your point b). Maybe you'd better use a classic minimum-cut approach, by modifying a measure such as the conductance in a way similar to that of Chen et al.
For your point c), I must say I never met this type of constraint before, and I find it very interesting. I guess you could try to perform some bi-criterion optimization, trying to minimize both conductance (or modularity) and a criterion such as the average distance to the closest source. But that would not guarantee the respect of point c). You can also force the number of detected communities so that it is less than the number of sources.
I have a list of about 100 igraph objects with a typical object having about 700 vertices and 3500 edges.
I would like to identify groups of vertices within which ties are more likely. My plan is to then use a mixed model to predict how many within-group ties vertices have using vertex and group attributes.
Some people may want to respond to other aspects of my project, which would be great, but the thing I'm most interested in is information about functions in igraph for grouping vertices. I've come across these community detection algorithms but I'm not sure of their advantages and disadvantages, or whether some other function would be better for my case. I saw the links here as well, but they aren't specific to igraph. Thanks for your advice.
Here is a short summary about the community detection algorithms currently implemented in igraph:
edge.betweenness.community is a hierarchical decomposition process where edges are removed in the decreasing order of their edge betweenness scores (i.e. the number of shortest paths that pass through a given edge). This is motivated by the fact that edges connecting different groups are more likely to be contained in multiple shortest paths simply because in many cases they are the only option to go from one group to another. This method yields good results but is very slow because of the computational complexity of edge betweenness calculations and because the betweenness scores have to be re-calculated after every edge removal. Your graphs with ~700 vertices and ~3500 edges are around the upper size limit of graphs that are feasible to be analyzed with this approach. Another disadvantage is that edge.betweenness.community builds a full dendrogram and does not give you any guidance about where to cut the dendrogram to obtain the final groups, so you'll have to use some other measure to decide that (e.g., the modularity score of the partitions at each level of the dendrogram).
fastgreedy.community is another hierarchical approach, but it is bottom-up instead of top-down. It tries to optimize a quality function called modularity in a greedy manner. Initially, every vertex belongs to a separate community, and communities are merged iteratively such that each merge is locally optimal (i.e. yields the largest increase in the current value of modularity). The algorithm stops when it is not possible to increase the modularity any more, so it gives you a grouping as well as a dendrogram. The method is fast and it is the method that is usually tried as a first approximation because it has no parameters to tune. However, it is known to suffer from a resolution limit, i.e. communities below a given size threshold (depending on the number of nodes and edges if I remember correctly) will always be merged with neighboring communities.
walktrap.community is an approach based on random walks. The general idea is that if you perform random walks on the graph, then the walks are more likely to stay within the same community because there are only a few edges that lead outside a given community. Walktrap runs short random walks of 3-4-5 steps (depending on one of its parameters) and uses the results of these random walks to merge separate communities in a bottom-up manner like fastgreedy.community. Again, you can use the modularity score to select where to cut the dendrogram. It is a bit slower than the fast greedy approach but also a bit more accurate (according to the original publication).
spinglass.community is an approach from statistical physics, based on the so-called Potts model. In this model, each particle (i.e. vertex) can be in one of c spin states, and the interactions between the particles (i.e. the edges of the graph) specify which pairs of vertices would prefer to stay in the same spin state and which ones prefer to have different spin states. The model is then simulated for a given number of steps, and the spin states of the particles in the end define the communities. The consequences are as follows: 1) There will never be more than c communities in the end, although you can set c to as high as 200, which is likely to be enough for your purposes. 2) There may be less than c communities in the end as some of the spin states may become empty. 3) It is not guaranteed that nodes in completely remote (or disconencted) parts of the networks have different spin states. This is more likely to be a problem for disconnected graphs only, so I would not worry about that. The method is not particularly fast and not deterministic (because of the simulation itself), but has a tunable resolution parameter that determines the cluster sizes. A variant of the spinglass method can also take into account negative links (i.e. links whose endpoints prefer to be in different communities).
leading.eigenvector.community is a top-down hierarchical approach that optimizes the modularity function again. In each step, the graph is split into two parts in a way that the separation itself yields a significant increase in the modularity. The split is determined by evaluating the leading eigenvector of the so-called modularity matrix, and there is also a stopping condition which prevents tightly connected groups to be split further. Due to the eigenvector calculations involved, it might not work on degenerate graphs where the ARPACK eigenvector solver is unstable. On non-degenerate graphs, it is likely to yield a higher modularity score than the fast greedy method, although it is a bit slower.
label.propagation.community is a simple approach in which every node is assigned one of k labels. The method then proceeds iteratively and re-assigns labels to nodes in a way that each node takes the most frequent label of its neighbors in a synchronous manner. The method stops when the label of each node is one of the most frequent labels in its neighborhood. It is very fast but yields different results based on the initial configuration (which is decided randomly), therefore one should run the method a large number of times (say, 1000 times for a graph) and then build a consensus labeling, which could be tedious.
igraph 0.6 will also include the state-of-the-art Infomap community detection algorithm, which is based on information theoretic principles; it tries to build a grouping which provides the shortest description length for a random walk on the graph, where the description length is measured by the expected number of bits per vertex required to encode the path of a random walk.
Anyway, I would probably go with fastgreedy.community or walktrap.community as a first approximation and then evaluate other methods when it turns out that these two are not suitable for a particular problem for some reason.
A summary of the different community detection algorithms can be found here: http://www.r-bloggers.com/summary-of-community-detection-algorithms-in-igraph-0-6/
Notably, the InfoMAP algorithm is a recent newcomer that could be useful (it supports directed graphs too).
Suppose I am given a weighted, connected graph. I'd like to find a list of edges that can be removed from the graph leaving it split into two components and so that the sum of the weights of the removed edges is small. Ideally I'd like to have the minimal sum, but I'd settle for a reasonable approximation.
This seems like a hard problem. Are there any good algorithms for doing this?
If it helps, in my case the number of nodes is about 50 and the graph may be dense, so that most pairs of nodes will have an edge between them.
I think you are looking for a minimum cut algorithm. Wikipedia
Before the Edmunds-Karp algorithm came the Ford-Fulkerson algorithm. For what it's worth, the Algorithms book [Cormen, Rivest] cites these two algorithms in the chapter on graph theory.
I believe what you're looking for is an algorithm for computing the minimum cut. The Edmonds-Karp algorithm does this for flow networks (with source and sink vertices). Hao and Orlin (1994) generalize this to directed, weighted graphs. Their algorithm runs in O(nm lg(n^2/m)) for n vertices and m edges. Chekuri et al. (1997) compare several algorithms empirically, some of which have better big O's than Hao and Orlin.
I may be wrong with my idea, but Ford Fulkersonalgorithm does not find a solution for this problem, since Ford Fulkerson assumes that there are source and destination nodes, and there is an attempt to transfer a material from source to destination. Hence, the algorithm cannot calculate all possible min-cuts.