In case you want a dynamic library be sure to then install the C core library from source before. Make sure you install the same versions. There is no standalone version of leidenalg , and you will always need python to access it. There are no plans at the moment for developing a standalone version or R support. However, there have been various efforts to port the package to R.
These typically do not offer all available functionality or have some other limitations, but nonetheless may be very useful. The available ports are:. This implementation is made for flexibility, but igraph nowadays also includes an implementation of the Leiden algorithm internally. That implementation is less flexible: the implementation only works on undirected graphs, and only CPM and modularity are supported. It is likely to be substantially faster though.
Just to get you started, below the essential parts. To start, make sure to import the packages:. See the documentation on Implementation for more details on how to contribute new methods. Skip to content. Star Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats commits. Failed to load latest commit information.
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Finally, we compare the performance of the algorithms on the empirical networks. We find that the Leiden algorithm commonly finds partitions of higher quality in less time.
The difference in computational time is especially pronounced for larger networks, with Leiden being up to 20 times faster than Louvain in empirical networks. We study the problem of badly connected communities when using the Louvain algorithm for several empirical networks.
For each community in a partition that was uncovered by the Louvain algorithm, we determined whether it is internally connected or not. In addition, to analyse whether a community is badly connected, we ran the Leiden algorithm on the subnetwork consisting of all nodes belonging to the community. We ensured that modularity optimisation for the subnetwork was fully consistent with modularity optimisation for the whole network 13 The Leiden algorithm was run until a stable iteration was obtained.
When the Leiden algorithm found that a community could be split into multiple subcommunities, we counted the community as badly connected. Note that if Leiden finds subcommunities, splitting up the community is guaranteed to increase modularity. Conversely, if Leiden does not find subcommunities, there is no guarantee that modularity cannot be increased by splitting up the community.
Hence, by counting the number of communities that have been split up, we obtained a lower bound on the number of communities that are badly connected. The count of badly connected communities also included disconnected communities. For each network, we repeated the experiment 10 times. As can be seen in Fig. Badly connected communities. Percentage of communities found by the Louvain algorithm that are either disconnected or badly connected compared to percentage of badly connected communities found by the Leiden algorithm.
Note that communities found by the Leiden algorithm are guaranteed to be connected. Later iterations of the Louvain algorithm only aggravate the problem of disconnected communities, even though the quality function i.
The second iteration of Louvain shows a large increase in the percentage of disconnected communities. In subsequent iterations, the percentage of disconnected communities remains fairly stable. The increase in the percentage of disconnected communities is relatively limited for the Live Journal and Web of Science networks. Other networks show an almost tenfold increase in the percentage of disconnected communities.
The percentage of badly connected communities is less affected by the number of iterations of the Louvain algorithm. Presumably, many of the badly connected communities in the first iteration of Louvain become disconnected in the second iteration. Indeed, the percentage of disconnected communities becomes more comparable to the percentage of badly connected communities in later iterations.
Nonetheless, some networks still show large differences. The above results shows that the problem of disconnected and badly connected communities is quite pervasive in practice. Because the percentage of disconnected communities in the first iteration of the Louvain algorithm usually seems to be relatively low, the problem may have escaped attention from users of the algorithm.
However, focussing only on disconnected communities masks the more fundamental issue: Louvain finds arbitrarily badly connected communities. The high percentage of badly connected communities attests to this. Besides being pervasive, the problem is also sizeable.
In the worst case, almost a quarter of the communities are badly connected. This may have serious consequences for analyses based on the resulting partitions.
For example, nodes in a community in biological or neurological networks are often assumed to share similar functions or behaviour However, if communities are badly connected, this may lead to incorrect attributions of shared functionality. Similarly, in citation networks, such as the Web of Science network, nodes in a community are usually considered to share a common topic 26 , Again, if communities are badly connected, this may lead to incorrect inferences of topics, which will affect bibliometric analyses relying on the inferred topics.
In short, the problem of badly connected communities has important practical consequences. The Leiden algorithm has been specifically designed to address the problem of badly connected communities. Figure 4 shows how well it does compared to the Louvain algorithm. The Leiden algorithm guarantees all communities to be connected, but it may yield badly connected communities.
In terms of the percentage of badly connected communities in the first iteration, Leiden performs even worse than Louvain, as can be seen in Fig. Crucially, however, the percentage of badly connected communities decreases with each iteration of the Leiden algorithm.
Starting from the second iteration, Leiden outperformed Louvain in terms of the percentage of badly connected communities. In fact, if we keep iterating the Leiden algorithm, it will converge to a partition without any badly connected communities, as discussed earlier.
Hence, the Leiden algorithm effectively addresses the problem of badly connected communities. To study the scaling of the Louvain and the Leiden algorithm, we rely on a variant of a well-known approach for constructing benchmark networks We generated benchmark networks in the following way. First, we created a specified number of nodes and we assigned each node to a community.
Communities were all of equal size. A community size of 50 nodes was used for the results presented below, but larger community sizes yielded qualitatively similar results.
We applied the Louvain and the Leiden algorithm to exactly the same networks, using the same seed for the random number generator. For both algorithms, 10 iterations were performed.
We used the CPM quality function. For each set of parameters, we repeated the experiment 10 times. As shown in Fig. The differences are not very large, which is probably because both algorithms find partitions for which the quality is close to optimal, related to the issue of the degeneracy of quality functions Scaling of benchmark results for network size. Speed and quality of the Louvain and the Leiden algorithm for benchmark networks of increasing size two iterations.
The Leiden algorithm is clearly faster than the Louvain algorithm. Figure 6 presents total runtime versus quality for all iterations of the Louvain and the Leiden algorithm. As can be seen in the figure, Louvain quickly reaches a state in which it is unable to find better partitions. A number of iterations of the Leiden algorithm can be performed before the Louvain algorithm has finished its first iteration. Later iterations of the Louvain algorithm are very fast, but this is only because the partition remains the same.
Runtime versus quality for benchmark networks. The horizontal axis indicates the cumulative time taken to obtain the quality indicated on the vertical axis. Each point corresponds to a certain iteration of an algorithm, with results averaged over 10 experiments. In general, Leiden is both faster than Louvain and finds better partitions. Scaling of benchmark results for difficulty of the partition. Analyses based on benchmark networks have only a limited value because these networks are not representative of empirical real-world networks.
In particular, benchmark networks have a rather simple structure. Empirical networks show a much richer and more complex structure. We now compare how the Leiden and the Louvain algorithm perform for the six empirical networks listed in Table 2.
For each network, Table 2 reports the maximal modularity obtained using the Louvain and the Leiden algorithm. In the first iteration, Leiden is roughly 2—20 times faster than Louvain. The speed difference is especially large for larger networks. This is similar to what we have seen for benchmark networks. However, Leiden is more than 7 times faster for the Live Journal network, more than 11 times faster for the Web of Science network and more than 20 times faster for the Web UK network.
First iteration runtime for empirical networks. Speed of the first iteration of the Louvain and the Leiden algorithm for six empirical networks.
Leiden is faster than Louvain especially for larger networks. Runtime versus quality for empirical networks. Speed and quality for the first 10 iterations of the Louvain and the Leiden algorithm for six empirical networks. Leiden is both faster than Louvain and finds better partitions. For all networks, Leiden identifies substantially better partitions than Louvain.
Louvain quickly converges to a partition and is then unable to make further improvements. In contrast, Leiden keeps finding better partitions in each iteration. The quality improvement realised by the Leiden algorithm relative to the Louvain algorithm is larger for empirical networks than for benchmark networks. Hence, the complex structure of empirical networks creates an even stronger need for the use of the Leiden algorithm.
Leiden keeps finding better partitions for empirical networks also after the first 10 iterations of the algorithm. This contrasts to benchmark networks, for which Leiden often converges after a few iterations.
For empirical networks, it may take quite some time before the Leiden algorithm reaches its first stable iteration. The DBLP network is somewhat more challenging, requiring almost 80 iterations on average to reach a stable iteration. The Web of Science network is the most difficult one.
For this network, Leiden requires over iterations on average to reach a stable iteration. Importantly, the first iteration of the Leiden algorithm is the most computationally intensive one, and subsequent iterations are faster.
For example, for the Web of Science network, the first iteration takes about — seconds, while subsequent iterations require about 40 seconds. Number of iterations until stability. Number of iterations before the Leiden algorithm has reached a stable iteration for six empirical networks. Community detection is an important task in the analysis of complex networks.
Finding communities in large networks is far from trivial: algorithms need to be fast, but they also need to provide high-quality results. One of the most widely used algorithms is the Louvain algorithm 10 , which is reported to be among the fastest and best performing community detection algorithms 11 , However, as shown in this paper, the Louvain algorithm has a major shortcoming: the algorithm yields communities that may be arbitrarily badly connected.
Communities may even be disconnected. Wij leveren tevens het volledige assortiment van o. Tools-n-More B. Wij bieden u de mogelijkheid om artikelen direct af te halen c. Tel: of bereikbaar tijdens de openingstijden van de winkel. De foto's kunnen, in sommige gevallen, enigzins afwijken van de werkelijkheid. Fruitweg Vind artikel:.
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