Label propagation algorithm
 | This article may be too technical for most readers to understand. (June 2015) |
Within complex networks, real networks tend to have community structure. Label propagation is an algorithm [1] for finding communities. In comparison with other algorithms[2] label propagation has advantage in its running time, amount of a priori information needed about the network structure (no parameter is required to be known beforehand). Disadvantage is that it produces no unique solution, but an aggregate of many solutions.
Functioning of the Algorithm
At initial condition, nodes carry a label that denotes the community they belong. Belonging to a community changes, based on the labels that the neighboring nodes possess. This change is subject to the maximum number of labels within one degree of the nodes. Every node is initialized with a unique label then the labels diffuse through the network. Consequently, densely connected groups reach a common label quickly. When many such dense (consensus) groups are created throughout the network, they continue to expand outwards until it is possible to do so.[1]
The process in 5 steps:[1]
1. Initialize the labels at all nodes in the network. For a given node x, Cx (0) = x.
2. Set t = 1.
3. Arrange the nodes in the network in a random order and set it to X.
4. For each x ∈ X chosen in that specific order, let Cx(t) = f(Cxi1(t), ...,Cxim(t),Cxi(m+1) (t − 1), ...,Cxik (t − 1)). f here returns the label occurring with the highest frequency among neighbours.
5. If every node has a label that the maximum number of their neighbours have, then stop the algorithm. Else, set t = t + 1 and go to (3).
Multiple Community Structure
In contrast with other algorithms label propagation can result in various community structures from the same initial condition. The range of solutions can be narrowed if some nodes are given preliminary labels while others are held unlabelled. Consequently, unlabelled nodes will be more likely to adapt to the labelled ones. For a more accurate finding of communities, Jaccard’s index is used to aggregate multiple community structures, containing all important information.[1]
References
- ^ a b c d U.N.Raghavan – R. Albert – S. Kumara "Near linear time algorithm to detect community structures in large-scale networks", 2007
- ^ M. E. J. Newman, "Detecting community structure in networks", 2004
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