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1. Local Correlation Clustering with Asymmetric Classification Errors

   Jafarov, Jafar; Kalhan, Sanchit; Makarychev, Konstantin; Makarychev, Yury (January 2021, International Conference on Machine Learning)

   null (Ed.)

   In the Correlation Clustering problem, we are given a complete weighted graph G with its edges labeled as “similar" and “dissimilar" by a noisy binary classifier. For a clustering C of graph G, a similar edge is in disagreement with C, if its endpoints belong to distinct clusters; and a dissimilar edge is in disagreement with C if its endpoints belong to the same cluster. The disagreements vector is a vector indexed by the vertices of G such that the v-th coordinate of the disagreements vector equals the weight of all disagreeing edges incident on v. The goal is to produce a clustering that minimizes the ℓp norm of the disagreements vector for p≥1. We study the ℓ_p objective in Correlation Clustering under the following assumption: Every similar edge has weight in [αw,w] and every dissimilar edge has weight at least αw (where α ≤ 1 and w > 0 is a scaling parameter). We give an O((1/α)^{1/2−1/(2p)} log 1/α) approximation algorithm for this problem. Furthermore, we show an almost matching convex programming integrality gap. 

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2. Local Correlation Clustering with Asymmetric Classification Errors

   Jafarov, Jafar; Kalhan, Sanchit; Makarychev, Konstantin; Makarychev, Yury (January 2021, Proceedings of Machine Learning Research)

   Meila, Marina; Zhang, Tong (Ed.)

   In the Correlation Clustering problem, we are given a complete weighted graph $$G$$ with its edges labeled as “similar" and “dissimilar" by a noisy binary classifier. For a clustering $$\mathcal{C}$$ of graph $$G$$, a similar edge is in disagreement with $$\mathcal{C}$$, if its endpoints belong to distinct clusters; and a dissimilar edge is in disagreement with $$\mathcal{C}$$ if its endpoints belong to the same cluster. The disagreements vector, $$\mathbf{disagree}$$, is a vector indexed by the vertices of $$G$$ such that the $$v$$-th coordinate $$\mathbf{disagree}_v$$ equals the weight of all disagreeing edges incident on $$v$$. The goal is to produce a clustering that minimizes the $$\ell_p$$ norm of the disagreements vector for $$p\geq 1$$. We study the $$\ell_p$$ objective in Correlation Clustering under the following assumption: Every similar edge has weight in $$[\alpha\mathbf{w},\mathbf{w}]$$ and every dissimilar edge has weight at least $$\alpha\mathbf{w}$$ (where $$\alpha \leq 1$$ and $$\mathbf{w}>0$$ is a scaling parameter). We give an $$O\left((\frac{1}{\alpha})^{\frac{1}{2}-\frac{1}{2p}}\cdot \log\frac{1}{\alpha}\right)$$ approximation algorithm for this problem. Furthermore, we show an almost matching convex programming integrality gap. 

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3. Correlation Clustering with Asymmetric Classification Errors

   Jafarov, Jafar; Kalhan, Sanchit; Makarychev, Konstantin; Makarychev, Yury (January 2020, International Conference on Machine Learning)

   In the Correlation Clustering problem, we are given a weighted graph $$G$$ with its edges labelled as "similar" or "dissimilar" by a binary classifier. The goal is to produce a clustering that minimizes the weight of "disagreements": the sum of the weights of "similar" edges across clusters and "dissimilar" edges within clusters. We study the correlation clustering problem under the following assumption: Every "similar" edge $$e$$ has weight $$w_e \in [ \alpha w, w ]$$ and every "dissimilar" edge $$e$$ has weight $$w_e \geq \alpha w$$ (where $$\alpha \leq 1$$ and $w > 0$ is a scaling parameter). We give a $$(3 + 2 \log_e (1/\alpha))$$ approximation algorithm for this problem. This assumption captures well the scenario when classification errors are asymmetric. Additionally, we show an asymptotically matching Linear Programming integrality gap of $$\Omega(\log 1/\alpha)$$ 

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4. A Weighted Approach to the Maximum Cardinality Bipartite Matching Problem with Applications in Geometric Settings

   Lahn, Nathaniel; Raghvendra, Sharath (June 2019, Leibniz international proceedings in informatics)

   We present a weighted approach to compute a maximum cardinality matching in an arbitrary bipartite graph. Our main result is a new algorithm that takes as input a weighted bipartite graph G(A cup B,E) with edge weights of 0 or 1. Let w <= n be an upper bound on the weight of any matching in G. Consider the subgraph induced by all the edges of G with a weight 0. Suppose every connected component in this subgraph has O(r) vertices and O(mr/n) edges. We present an algorithm to compute a maximum cardinality matching in G in O~(m(sqrt{w} + sqrt{r} + wr/n)) time. When all the edge weights are 1 (symmetrically when all weights are 0), our algorithm will be identical to the well-known Hopcroft-Karp (HK) algorithm, which runs in O(m sqrt{n}) time. However, if we can carefully assign weights of 0 and 1 on its edges such that both w and r are sub-linear in n and wr=O(n^{gamma}) for gamma < 3/2, then we can compute maximum cardinality matching in G in o(m sqrt{n}) time. Using our algorithm, we obtain a new O~(n^{4/3}/epsilon^4) time algorithm to compute an epsilon-approximate bottleneck matching of A,B subsetR^2 and an 1/(epsilon^{O(d)}}n^{1+(d-1)/(2d-1)}) poly log n time algorithm for computing epsilon-approximate bottleneck matching in d-dimensions. All previous algorithms take Omega(n^{3/2}) time. Given any graph G(A cup B,E) that has an easily computable balanced vertex separator for every subgraph G'(V',E') of size |V'|^{delta}, for delta in [1/2,1), we can apply our algorithm to compute a maximum matching in O~(mn^{delta/1+delta}) time improving upon the O(m sqrt{n}) time taken by the HK-Algorithm. 

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5. Locally Computing Edge Orientations

   Mitrovic, Slobodan; Rubinfeld, Ronitt; Singhal, Mihir (September 2024, 32nd Annual European Symposium on Algorithms (ESA 2024))

   We consider the question of orienting the edges in a graph G such that every vertex has bounded out-degree. For graphs of arboricity α, there is an orientation in which every vertex has out-degree at most α and, moreover, the best possible maximum out-degree of an orientation is at least α - 1. We are thus interested in algorithms that can achieve a maximum out-degree of close to α. A widely studied approach for this problem in the distributed algorithms setting is a "peeling algorithm" that provides an orientation with maximum out-degree α(2+ε) in a logarithmic number of iterations. We consider this problem in the local computation algorithm (LCA) model, which quickly answers queries of the form "What is the orientation of edge (u,v)?" by probing the input graph. When the peeling algorithm is executed in the LCA setting by applying standard techniques, e.g., the Parnas-Ron paradigm, it requires Ω(n) probes per query on an n-vertex graph. In the case where G has unbounded degree, we show that any LCA that orients its edges to yield maximum out-degree r must use Ω(√ n/r) probes to G per query in the worst case, even if G is known to be a forest (that is, α = 1). We also show several algorithms with sublinear probe complexity when G has unbounded degree. When G is a tree such that the maximum degree Δ of G is bounded, we demonstrate an algorithm that uses Δ n^{1-log_Δ r + o(1)} probes to G per query. To obtain this result, we develop an edge-coloring approach that ultimately yields a graph-shattering-like result. We also use this shattering-like approach to demonstrate an LCA which 4-colors any tree using sublinear probes per query. 

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