We present a probabilistic algorithm that, given a connected graph G (represented by adjacency lists) of average degree d, with edge weights in the set {1,...,w}, and given a parameter 0 < ε < 1/2, estimates in time O(dwε−2 log dw ε) the weight of the minimum span-ning tree of G with a relative error of at most ε. Note that the running time does not depend on the number of vertices in G. We also prove a nearly matching lower bound of Ω(dwε−2) on the probe and time complexity of any approximation algorithm for MST weight. The essential component of our algorithm is a procedure for estimating in time O(dε−2 log d ε) the number of connected components of an unweighted graph to within an additive error of εn. (This becomes O(ε−2 log 1 ε) for d = O(1).) The time bound is shown to be tight up to within the log d ε factor. Our connected-components algorithm picks O(1/ε2) vertices in the graph and then grows “local spanning trees” whose sizes are specified by a stochastic process. From the local information collected in this way, the algorithm is able to infer, with high confidence, an estimate of the number of connected components. We then show how estimates on the number of components in various subgraphs of G can be used to estimate the weight of its MST. 1

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In this paper we consider the problem of testing bipartiteness of general graphs. The problem has previously been studied in two models, one most suitable for dense graphs, and one most suitable for bounded-degree graphs. Roughly speaking, dense graphs can be tested for bipartiteness with constant complexity, while the complexity of testing bounded-degree graphs is ˜ Θ ( √ n), where n is the number of vertices in the graph (and ˜ Θ(f(n)) means Θ(f(n) · polylog(f(n)))). Thus there is a large gap between the complexity of testing in the two cases. In this work we bridge the gap described above. In particular, we study the problem of testing bipartiteness in a model that is suitable for all densities. We present an algorithm whose complexity is Õ(min( √ n, n 2 /m)) where m is the number of edges in the graph, and match it with an almost tight lower bound. This work is part of the author’s Ph.D. thesis prepared at Tel Aviv University under the supervision of Prof.

Property testing algorithms are “ultra”-efficient algorithms that decide whether a given object (e.g., a graph) has a certain property (e.g., bipartiteness), or is significantly different from any object that has the property. To this end property testing algorithms are given the ability to perform (local) queries to the input, though the decision they need to make usually concern properties with a global nature. In the last two decades, property testing algorithms have been designed for many types of objects and properties, amongst them, graph properties, algebraic properties, geometric properties, and more. In this article we survey results in property testing, where our emphasis is on common analysis and algorithmic techniques. Among the techniques surveyed are the following: • The self-correcting approach, which was mainly applied in the study of property testing of algebraic properties; • The enforce and test approach, which was applied quite extensively in the analysis of algorithms for testing graph properties (in the dense-graphs model), as well as in other contexts;

I would like to thank Eyal for his endless guidance, devotion and encouragement, and above all for his support and sense of humor. I will like to thank my wonderful family that accompanies me in every road I take, and unconditionally supports me in every decision I make. Especially, I like to thank my husband Yuval, my partner for the journey of life, for hearing me all the time and sometimes even listening. The generous financial help of the Technion is gratefully acknowledged.

The strive for efficiency is ancient and universal, as time is always short for humans. Computational Complexity is a mathematical study of the what can be achieved when time (and other resources) are scarce. In this

We continue the study, started in [9], of property testing of graphs in the orientation model. A major question which was left open in [9] is whether the property of st-connectivity can be tested with a constant number of queries. Here we answer this question on the affirmative. To this end we construct a non-trivial reduction of the st-connectivity problem to the problem of testing languages that are decidable by branching programs, which was solved in [11]. The reduction combines combinatorial arguments with a concentration type lemma that is proven for this purpose. Unlike many other property testing results, here the resulting testing algorithm is highly non-trivial itself, and not only its analysis.

In model checking, program correctness on all inputs is verified by considering the transition system underlying a given program. In practice, the system can be intractably large. In property testing, a property of a single input is verified by looking at a small subset of that input. We join the strengths of both approaches by introducing to model checking the notion of probabilistic abstraction. We put forth the notion of "-reducibility which is implicit in many property testers. Our probabilistic abstraction associates a set of small random transition systems to a program. Under some conditions, these transition systems are sufficient to guarantee that a program approximately decides on all its inputs a property like bipartiteness, k-colorability, or any first order graph properties of type 98. We give a concrete example of an abstraction for a program which decides bipartiteness. Finally, we show that abstraction is necessary by proving an exponential lower bound on OBDDs for approximate bipartiteness.

A primer to property testing

Detecting and counting the number of copies of certain subgraphs (also known as network motifs or graphlets), is motivated by applications in a variety of areas ranging from Biology to the study of the World-Wide-Web. Several polynomial-time algorithms have been suggested for counting or detecting the number of occurrences of certain network motifs. However, a need for more efficient algorithms arises when the input graph is very large, as is indeed the case in many applications of motif counting. In this paper we design sublinear-time algorithms for approximating the number of copies of certain constant-size subgraphs in a graph G. That is, our algorithms do not read the whole graph, but rather query parts of the graph. Specifically, we consider algorithms that may query the degree of any vertex of their choice and may ask for any neighbor of any vertex of their choice. The main focus of this work is on the basic problem of counting the number of length-2 paths and more generally on counting the number of stars of a certain size. Specifically, we design an algorithm that, given an approximation parameter 0 < ɛ < 1 and query access to a graph G, outputs an estimate ˆνs such that with high constant probability, (1−ɛ)νs(G) ≤ ˆνs ≤ (1+ɛ)νs(G), where νs(G) denotes the number of stars of size s + 1 in the graph. The expected query ( complexity and { running time of}) the algorithm are O