He Sun, Peter Macgregor
We lifted 15 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| pmacg/local-densely-connected-clusters | canonical | 0 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| MQI | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/MQI.py code served (permissive licence) · get_code("e1737d8b1e0223d9") |
| MQI_weighted | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/MQI_weighted.py code served (permissive licence) · get_code("a3be98a2ac73b7e5") |
| SimpleLocal | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/SimpleLocal.py code served (permissive licence) · get_code("f3ee78284e072922") |
| SimpleLocal_weighted | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/SimpleLocal_weighted.py code served (permissive licence) · get_code("5b7985e7d25d8368") |
| acl_list | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/algorithms/acl_list.py code served (permissive licence) · get_code("25afa867451dfa44") |
| compute_embedding | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/find_clusters.py code served (permissive licence) · get_code("8bebfc6664287a78") |
| compute_embedding_and_improve | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/find_clusters.py code served (permissive licence) · get_code("2f40ada12f80b4c5") |
| eig2nL_subgraph | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/algorithms/eig_nL.py code served (permissive licence) · get_code("3bc99562f219b3ab") |
| eig_nL | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/algorithms/eig_nL.py code served (permissive licence) · get_code("0bd42037d5751844") |
| evo_cut_directed | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/find_bipartite_clusters.py code served (permissive licence) · get_code("240e3c7e162a8bd2") |
| find_clusters | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/find_clusters.py code served (permissive licence) · get_code("56e5317203aecca6") |
| idx_from_cluster_idx | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/stochastic_block_model.py code served (permissive licence) · get_code("2a003b58e847d218") |
| local_bipartite_dc | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/find_bipartite_clusters.py code served (permissive licence) · get_code("921c9fe10dabbcfd") |
| lp_almost_bipartite | Not yet run | pmacg/local-densely-connected-clusters/localgraphclustering/find_bipartite_clusters.py code served (permissive licence) · get_code("4c8c39f3a5fdcba7") |
| simplify | Not yet run | pmacg/local-densely-connected-clusters/example.py code served (permissive licence) · get_code("e6d355edc09c35ec") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Local graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science. While the objective of most (local) graph clustering algorithms is to find a vertex set of low conductance, there has been a sequence of recent studies that highlight the importance of the inter-connection between clusters when analysing real-world datasets. Following this line of research, in this work we study local algorithms for finding a pair of vertex sets defined with respect to their inter-connection and their relationship with the rest of the graph. The key to our analysis is a new reduction technique that relates the structure of multiple sets to a single vertex set in the reduced graph. Among many potential applications, we show that our algorithms successfully recover densely connected clusters in the Interstate Disputes Dataset and the US Migration Dataset.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.05245")
get_code_for_paper("2106.05245")
have("2106.05245")
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