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Just a few remarks from me, ### Definition of mountain Why is the k-peak in the k-mountain ? Why is the core number of a k-peak changed when removing the k+1 peak ? ###More informations on k-mountains Showing a k-mountain on figure 2 or on another toy exemple would have been nice. Besides, more theoretical insights on k-mountains are missing : complexity in time and space of the algorithm used to find the k-mountains ? How many k-mountains include a given node ? Less than the degeneracy of the graph but maybe it's possible to say more. Regarding the complexity of the algorithm used to build the k-mountains, I'm Not sure I understand everything but when the k-peak decomposition is done, for each k-contour, a new k-core decomposition must be run on the subgraph obtained when removing the k-contour. Then it must be compared whith the k-core decomposition of the whole graph. All these operations take time so the complexity of the algorithm used to build the k-mountains is I think higher than the complexity of the k-peak decomposition. Besides multiple choices are made for building the k-mountains, it seems to me that this tool deserves a broader analysis. Same thing for the mountains shapes, some insights are missing, for instance mountains may have an obvious staircase shape, does it mean something ? ###6.2 _p_ I don't get really get what is p? ###7.2 is unclear but it may be related to my ignorance of what is a protein.
### Lemma 2 (bis): Lemma 2 (bis): Algorithm 1 requires $O(\delta\cdot(N+M))$ time, where $\delta$ is the degeneracy of the input graph. Proof: $k_1=\delta$ and the $k_i$ values must be unique non-negative integers, there are thus $\delta$ distinct $k_i$ values or less. Thus, Algorithm 1 enters the while loop $\delta$ times or less leading to the stated running time. We thus have a running time in $O(\min(\sqrt{N},\delta)\cdot (M+N))$. Note that $\delta \leq \sqrt{M}$ and $\delta<N$ and in practice (in large sparse real-world graphs) it seems that $\delta\lessapprox \sqrt{N}$. ### k-core decomposition definition: The (full) definition of the k-core decomposition may come a bit late. Having an informal definition of the k-core decomposition (not just a definition of k-core) at the beginning of the introduction may help a reader not familiar with it. ### Scatter plots: Scatter plots: "k-core value VS k-peak value" for each node in the graph are not shown. This may be interesting. Note that scatter plots: "k-core value VS degree" are shown in "Kijung, Eliassi-Rad and Faloutsos. CoreScope: Graph Mining Using k-Core Analysis. ICDM2016" leading to interesting insights on graphs. Something similar could be done with k-peak. ###Experiments on large graphs: As the algorithm is very scalable: nearly linear time in practice (on large sparse real-world graphs) and linear memory. Experiments on larger graphs e.g. 1G edges could be done. ### Implementation: Even though the algorithm is very easy to implement, a link to a publicly available implementation would make the framework easier to use and easier to improve/extend. ### Link to the densest subgraph: The $\delta$-core (with $\delta$ the degeneracy of the graph) is a 2-approximation of the densest subgraph (here the density is defined as the average degree divided by 2) and thus the core decomposition can be seen as a (2-)approximation of the density friendly decomposition. - "Density-friendly graph decomposition". Tatti and Gionis. WWW2015. - "Large Scale Density-friendly Graph Decomposition via Convex Programming". Danisch et al. WWW2017. Having this in mind, the k-peak decomposition can be seen as an approximation of the following decomposition: - 1 Find the densest subgraph. - 2 Remove it from the graph (along with all edges connected to it). - 3 Go to 1 till the graph is empty. ### Faster algorithms: Another appealing feature of the k-core decomposition is that it is used to make faster algorithm. For instance, it is used in https://arxiv.org/abs/1006.5440 and to https://arxiv.org/abs/1103.0318 list all maximal cliques efficiently in sparse real-world graphs. Can the k-peak decomposition be used in a similar way to make some algorithms faster? ### Section 6.2 not very clear. ### Typos/Minors: - "one can view the the k-peak decomposition" - "et. al", "et al" - "network[23]" (many of these) - "properties similar to k-core ."
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## About scalability: "We apply a standard cleaning procedure (for similarity) and remove high out-degrees. In other words, if some vertex v has more than 10K followers (outdegree > 10K), we remove all these edges. (We do not remove the vertex, but rather only its out-edges.) Intuitively, the fact that two vertices are followed by v is not a useful signal for similarity. In flock and friendster, such vertices are typically spammers and should be ignored. For webgraphs, a page linking to more than 10K other pages is probably not useful for similarity measurement." - Typo: should be "more than 10K followeEs (outdegree > 10K)" not "followers". - Such a cleaning procedure makes sense, in addition, it has a valuable side effect: in practice, after applying such a cleaning procedure, many pairs of nodes with very small non-zero similarity (in the original directed graph) have a similarity of zero (in the new graph with the out-edges of the out-hubs removed). - Given that such a cleaning procedure is applied, a brute force approach (that computes the similarity of each pair of nodes sharing at least one in-neighbor) is scalable to a large extent. - The following C code and experiments support the previous claim: https://github.com/maxdan94/cosineSparseMatrix - If scalability is still an issue, in-edges of nodes with very high in-degree could also be removed. "In our example, this turns out to be more than 100 trillion triples. This is infeasible even for a large industrial-strength cluster.". Listing 100 trillion triples seems doable with a medium-strength cluster. "If a matrix A has a 100 billion non-zeroes, it takes upwards of 1TB just to store the entries. This is more than an order of magnitude of the storage of a commodity machine in a cluster. Any approach of partitioning A into submatrices cannot scale." Indeed, memory seems to be a problem. However, it is unclear why an approach partitioning A into submatrices cannot scale. ## Dataset: "The dataset friendster is a social network, and is available from the Stanford Large Network Dataset Collection [28]." It is unclear which friendster dataset is used. In SNAP, http://snap.stanford.edu/data/com-Friendster.html seems to be "undirected" and has 1.8B edges, not "1.6B edges" as written in Table 1 (even after removing edges of nodes with degree larger than 10,000). ## Typos and minors: - Inside the paper $O(100B)$ looks a bit strange, because according to [the definition](https://en.wikipedia.org/wiki/Big_O_notation#Formal_definition) the set $O(100B)$ is equal to $O(0.00001)$ - should be "more than 10K followeEs (outdegree > 10K)" not "followers".
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### Can be extended to k-cliques and k-motifs: - To list k-cliques: https://github.com/maxdan94/kClist - To list k-motifs: https://github.com/maxdan94/kmotif ### Typos: - ", cf.[23]," - "conductance problem.Notice that" - "(CNM) [12] , Cfinder" - "Louvaine" instead of "Louvain" in Table 2
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Great paper! ### Comparison to existing methods: Doing some experimental comparisons against [Eppstein and Wang 2004](https://papers-gamma.link/paper/35) for the closeness centrality might be interesting. ### Typos: - $|\mathscr{B}_{G}(v,t)|-|\mathscr{B}_{G}(v,t-1)|$ instead of $|\mathscr{B}_{G}(v,t+1)|-|\mathscr{B}_{G}(v,t)|$. This is corrected in the other formula on centralities. - "the the reciprocal of a" - "can be easily computed in a cumulative fashion nothing that" - "on the approximation the diameter" - "its importance it by 1/2" ### Minors: - "Nodes with empty coreachable set have centrality 1 by definition" . By definition the coreachable set of a node is never empty, it contains at least the concerned node.
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Feel free to start an awesome discussion. ### Typos: - "is defined as the the minimum"
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