Among the most important graph parameters is the Diameter, the largest distance between any two vertices. There are no known very efficient algorithms for computing the Diameter exactly. Thus, much research has been devoted to how fast this parameter can be approximated. Chechik et al. [SODA 2014] showed that the diameter can be approximated within a multiplica-* backurs@mit.edu, Supported by an IBM PhD Fellowship, the NSF and the Simons Foundation † liam.roditty@biu.ac.il ‡ Our lower bounds apply only to sparse graphs. We show that for dense graphs, there are near-linear time algorithms for S-T Diameter, Diameter and Eccentricities, with almost the same approximation guarantees as theirÕ(m 3/2 ) counterparts, improving upon the best known algorithms for dense graphs.

Among the most important graph parameters is the diameter, the largest distance between any two vertices. There are no known very efficient algorithms for computing the diameter exactly. Thus, much research has been devoted to how fast this parameter can be approximated. Chechik et al. [Proceedings of SODA 2014, Portland, OR, 2014, pp. 1041--1052 showed that the diameter can be approximated within a multiplicative factor of 3/2 in \Õ (m 3/2 ) time. Furthermore, Roditty and Vassilevska W. [Proceedings of STOC '13, New York, ACM, 2013, pp. 515--524] showed that unless the strong exponential time hypothesis (SETH) fails, no O(n 2 - \varepsi ) time algorithm can achieve an approximation factor better than 3/2 in sparse graphs. Thus the above algorithm is essentially optimal for sparse graphs for approximation factors less than 3/2. It was, however, completely plausible that a 3/2-approximation is possible in linear time. In this work we conditionally rule out such a possibility by showing that unless SETH fails no O(m 3/2 - \varepsi ) time algorithm can achieve an approximation factor better than 5/3. Another fundamental set of graph parameters is the eccentricities. The eccentricity of a vertex v is the distance between v and the farthest vertex from v. Chechik et al. [Proceedings of SODA 2014, Portland, OR, 2014, pp. 1041--1052 showed that the eccentricities of all vertices can be approximated within a factor of 5/3 in \Õ (m 3/2 ) time and Abboud, Vassilevska W., and Wang [Proceedings of SODA 2016, Arlington, VA, 2016 showed that no O(n 2 - \varepsi ) algorithm can achieve better than 5/3 approximation in sparse graphs. We show that the runtime of the 5/3 approximation algorithm is also optimal by proving that under SETH, there is no O(m 3/2 - \varepsi ) algorithm that achieves a better than 9/5 approximation. We also show that no near-linear time algorithm can achieve a better than 2 approximation for the eccentricities. This is the first lower bound in fine-grained complexity that addresses near-linear time computation. We show that our lower bound for near-linear time algorithms is essentially tight by giving an algorithm that approximates eccentricities within a 2 + \delta factor in \Õ (m/\delta ) time for any 0 < \delta < 1. This beats all eccentricity algorithms in Cairo, Grossi, and Rizzi [Proceedings of SODA 2016, Arlington, VA, 2016, pp. 363--376] and is the first constant factor approximation for eccentricities in directed graphs. To establish the above lower bounds we study the S-T diameter problem: Given a graph and two subsets S and T of vertices, output the largest distance between a vertex in S and a vertex in T . We give new algorithms and show tight lower bounds that serve as a starting point for all other hardness results. Our lower bounds apply only to sparse graphs. We show that for dense graphs, there are near-linear time algorithms for S-T diameter, diameter, and eccentricities, with almost the same approximation guarantees as their \Õ (m 3/2 ) counterparts, improving upon the best known al...

No abstract