Community Detection via Measure Space Embedding
Authors: Mark Kozdoba, Shie Mannor
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the algorithm on standard random graph benchmarks, including some overlapping community benchmarks, and find its performance to be better or at least as good as previously known algorithms. |
| Researcher Affiliation | Academia | Mark Kozdoba The Technion, Haifa, Israel markk@tx.technion.ac.il Shie Mannor The Technion, Haifa, Israel shie@ee.technion.ac.il |
| Pseudocode | Yes | Algorithm 1 DER 1: Input: Graph G, walk length L, number of components k. 2: Compute the measures wi. 3: Initialize P1, . . . , Pk to be a random partition such that |Pi| = |V |/k for all i. 4: repeat 5: (1) For all s k, construct µs = µPs. 6: (2) For all s k, set Ps = i V | s = argmax l D(wi, µl) . 7: until the sets Ps do not change |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for the source code of the methodology described. |
| Open Datasets | Yes | On the empirical side, we first evaluate our algorithm on a set of random graph benchmarks known as the LFR models, [3]. The LFR benchmark model, [14], is a widely used extension of the stochastic block model, where node degrees and community sizes have power law distribution, as often observed in real graphs. Figure 1a shows the classical Zachary s Karate Club, [22]. Figure 1b shows the political blogs graph, [23]. |
| Dataset Splits | No | The paper describes generating graphs from models and then evaluating on them, e.g., "For each combination of graph size, community size restrictions as above and µ value, we generated 20 graphs from that model and run DER." This indicates data generation and evaluation rather than explicit train/validation/test splits for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In all runs on DER we have set L = 5 and set k to be the true number of communities for each graph, as was done in [4] for the methods that required it. The DER algorithm was run with L = 2, and k was set to the true number of communities. |