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For a fast screening: Figure 1, Figure 4 and conclusion. Can we use a hierarchical tree directly as input to machine learning algorithms instead of vectors? Code: - https://github.com/maxdan94/LouvainNE - https://github.com/maxdan94/RandNE
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Nice paper along the work of [Dasgupta](https://papers-gamma.link/paper/155) and [Cohen-Addad et al.](https://arxiv.org/pdf/1704.02147.pdf). A function to quantify the quality of a hierarchical graph clustering / dendrogram is proposed. An interesting application to compress a dendrogram is proposed. Section 8. If the input graph is a complete bipartite graph, then the quality function Q is maximum if the graph is partitioned in the two independent sets.
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Apparently, a single 'mov' assembler instruction is already Turing complete. Implementation of mov-only compilator is [available](https://github.com/xoreaxeaxeax/movfuscator/blob/master/README.md). > The M/o/Vfuscator (short 'o', sounds like "mobfuscator") compiles programs into "mov" instructions, and only "mov" instructions. Arithmetic, comparisons, jumps, function calls, and everything else a program needs are all performed through mov operations; there is no self-modifying code, no transport-triggered calculation, and no other form of non-mov cheating.
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