The papers in this volume were selected for presentation at the Eleventh Annual International Symposium on Algorithms and Computation (ISAAC 2000), held on 18{20 December, 2000 at the Institute of Information Science, Academia Sinica, Taipei, Taiwan. Previous meetings were held in Tokyo (1990), Taipei (1991), Nagoya (1992), Hong Kong (1993), Beijing (1994), Cairns (1995), Osaka (1996), Singapore (1997), Taejon (1998), and Chennai (1999). Submissions to the conference this year were conducted entirely electro- cally. Thanks to the excellent software developed by the Institute of Information Science, Academia Sinica, we were able to carry out virtually all communication via the World Wide Web. In response to the call for papers, a total of 87 extended abstracts were submitted from 25 countries. Each submitted paper was handled by at least three program committee members, with the assistance of a number of external reviewers, as indicated by the referee list found in the proceedings. There were many more acceptable papers than there was space available in the symposium program, which made the program committee's task extremely di cult. Finally 46 papers were selected for presentation at the Symposium. In addition to these contributed papers, the conference also included two invited presentations by Dr. Jean-Daniel Boissonnat, INRIA Sophia-Antipolis, France and Professor Jin-Yi Cai, University of Wisconsin at Madison, Wisconsin, USA. It is expected that most of the accepted papers will appear in a more complete form in scienti c journals.

This book constitutes the refereed proceedings of the 5th International Symposium on Solving Irregularly Structured Problems in Parallel, IRREGULAR'98, held in Berkeley, California, in August 1998.
The 26 revised full papers presented were carefully reviewed and selected for inclusion from several dozen submissions. Also included are abstracts of four invited talks and 6 invited presentations given during minisymposia held in parallel. The book presents a unique overview on algorithmic, applicational, and systems aspects arising in the development of efficient parallel solutions to irregularly structured problems.

In the age of Big Data, efficient algorithms are in higher demand more than ever before. While Big Data takes us into the asymptotic world envisioned by our pioneers, it also challenges the classical notion of efficient algorithms: algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today's problems. It is not just desirable but essential that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. Scalable Algorithms for Data and Network Analysis surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning. They also include spectral graph-theoretical methods, such as are used for computing electrical flows and sampling from Gaussian Markov random fields. These methods exemplify the fusion of combinatorial, numerical, and statistical thinking in network analysis. This book illustrates the use of these techniques by a few basic problems that are fundamental in analyzing network data, particularly for the identification of significant nodes and coherent clusters/communities in social and information networks. It also discusses some frameworks beyond graph-theoretical models for studying conceptual questions that arise in network analysis and social influences.