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We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

/pdf/maximizing-the-spread-of-influence-through-a-social-network-47y1ehuvwi.pdf

Maximizing the Spread of Influence through a Social Network

Tables of integrals

1 Introduction 2 Map 3 The Data Stream Phenomenon 4 Data Streaming: Formal Aspects 5 Foundations: Basic Mathematical Ideas 6 Foundations: Basic Algorithmic Techniques 7 Foundations: Summary 8 Streaming Systems 9 New Directions 10 Historic Notes 11 Concluding Remarks Acknowledgements References

/pdf/data-streams-algorithms-and-applications-1046wbaqok.pdf

Data Streams: Algorithms and Applications

We consider the performance of two classic approximation algorithms which work by scanning the input and greedily constructing a solution. We investigate whether running these algorithms on a random permutation of the input can increase their performance ratio. We obtain the following results:1. Johnson's approximation algorithm for MAX-SAT is one of the first approximation algorithms to be rigorously analyzed. It has been shown that the performance ratio of this algorithm is 2/3. We show that when executed on a random permutation of the variables, the performance ratio of this algorithm is improved to 2/3 + c for some c > 0 This resolves an open problem of Chen, Friesen and Zhang [JCSS 1999]. (See also the paper by Poloczek and Schnitger in these proceedings for related results on this algorithm and its variants).2. Motivated by the above improvement, we consider the performance of the greedy algorithm for MAX-CUT whose performance ratio is 1/2. Our hope was that running the greedy algorithm on a random permutation of the vertices would result in a 1/2 + c approximation algorithm. However, it turns out that in this case the performance of the algorithm remains 1/2. This resolves an open problem of Mathieu and Schudy [SODA 2008].

https://epubs.siam.org/doi/pdf/10.1137/1.9781611973082.50

Randomized greedy: new variants of some classic approximation algorithms

Let f(n,v,e) denote the maximum number of edges in a 3-uniform hypergraph not containing e edges spanned by at most v vertices. One of the most influential open problems in extremal combinatorics then asks, for a given number of edges e≥3, what is the smallest integer d=d(e) so that f(n,e+d,e)=o(n²)? This question has its origins in work of Brown, Erdős and Sos from the early 70's and the standard conjecture is that d(e)=3 for every e≥3. The state of the art result regarding this problem was obtained in 2004 by Sarkozy and Selkow, who showed that f(n,e+2+⌊log₂e⌋,e)=o(n²). The only improvement over this result was a recent breakthrough of Solymosi and Solymosi, who improved the bound for d(10) from 5 to 4. We obtain the first asymptotic improvement over the Sarkozy--Selkow bound, showing that
f(n,e+O(loge/logloge),e)=o(n²).

A New Bound for the Brown--Erdős--Sós Problem

We introduce a new variant of Szemeredi's regularity lemma which we call the "sparse regular approximation lemma" (SRAL). The input to this lemma is a graph $G$ of edge density $p$ and parameters $\epsilon, \delta$, where we think of $\delta$ as a constant. The goal is to construct an $\epsilon$-regular partition of $G$ while having the freedom to add/remove up to $\delta |E(G)|$ edges. As we show here, this weaker variant of the regularity lemma already suffices for proving the graph removal lemma and the hypergraph regularity lemma, which are two of the main applications of the (standard) regularity lemma. This of course raises the following question: can one obtain quantitative bounds for SRAL that are significantly better than those associated with the regularity lemma? 
Our first result answers the above question affirmatively by proving an upper bound for SRAL given by a tower of height $O(\log 1/p)$. This allows us to reprove Fox's upper bound for the graph removal lemma. Our second result is a matching lower bound for SRAL showing that a tower of height $\Omega(\log 1/p)$ is unavoidable. We in fact prove a more general multicolored lower bound which is essential for proving lower bounds for the hypergraph regularity lemma.

A sparse regular approximation lemma

Let G=(V,E,w) be a directed graph, where w:V→ℝ is a weight function defined on its vertices. The bottleneck weight, or the capacity, of a path is the smallest weight of a vertex on the path. For two vertices u,v the capacity from u to v, denoted by c(u,v), is the maximum bottleneck weight of a path from u to v. In the All-Pairs Bottleneck Paths (APBP) problem the task is to find the capacities for all ordered pairs of vertices. Our main result is an O(n 2.575) time algorithm for APBP. The exponent is derived from the exponent of fast matrix multiplication.

A variant of our algorithm computes shortest paths of maximum bottleneck weight. Let d(u,v) denote the (unweighted) distance from u to v, and let sc(u,v) denote the maximum bottleneck weight of a path from u to v having length d(u,v). The All-Pairs Bottleneck Shortest Paths (APBSP) problem is to compute sc(u,v) for all ordered pairs of vertices. We present an algorithm for APBSP whose running time is O(n 2.86).

/pdf/all-pairs-bottleneck-paths-in-vertex-weighted-graphs-4h356s9822.pdf

All-Pairs Bottleneck Paths in Vertex Weighted Graphs

Szemeredi's regularity lemma is a cornerstone result in extremal combinatorics. It (roughly) asserts that any dense graph is composed of a finite number of pseudorandom graphs. The regularity lemma has found many applications in theoretical computer science, and thus a lot of attention was given to designing algorithmic versions of this lemma. Our main results in this paper are the following: (i) We introduce a new approach to the problem of constructing regular partitions of graphs, which results in a surprisingly simple $O(n)$ time algorithmic version of the regularity lemma, thus improving over the previous $O(n^2)$ time algorithms. Furthermore, unlike all the previous approaches for this problem (see [N. Alon and A. Naor, SIAM J. Comput., 35 (2006), pp. 787-803], [R. A. Duke, H. Lefmann, and V. Rodl, SIAM J. Comput., 24 (1995), pp. 598-620], [A. Frieze and R. Kannan, Electron. J. Combin., 6 (1999), article 17], [A. Frieze and R. Kannan, “The regularity lemma and approximation schemes for dense problems,” in Proceedings of the 37th Annual Symposium on Foundations of Computer Science (Burlington, VT, 1996), IEEE Computer Society Press, Los Alamitos, CA, 1996, pp. 12-20], and [Y. Kohayakawa, V. Rodl, and L. Thoma, SIAM J. Comput., 32 (2003), pp. 1210-1235]), which only guaranteed to find tower-size partitions, our algorithm will find a small regular partition, if one exists in the graph. (ii) For any constant $r\geq3$ we give an $O(n)$ time randomized algorithm for constructing regular partitions of $r$-uniform hypergraphs, thus improving the previous $O(n^{2r-1})$ time (deterministic) algorithms [A. Czygrinow and V. Rodl, SIAM J. Comput., 30 (2000), pp. 1041-1066], [A. Frieze and R. Kannan, “The regularity lemma and approximation schemes for dense problems,” in Proceedings of the 37th Annual Symposium on Foundations of Computer Science (Burlington, VT, 1996), IEEE Computer Society Press, Los Alamitos, CA, 1996, pp. 12-20]. These two results are obtained as an application of an efficient algorithm for approximating partition problems of hypergraphs which we obtain here: Given a (directed) hypergraph with bounded edge arities, a set of constraints on the set sizes and densities of a possible partition of its vertex set, and an approximation parameter, we provide in $O(n)$ time a partition approximating the constraints if a partition satisfying them exists. We can also test in $O(1)$ time for the existence of such a partition given the approximation parameter. This algorithm extends the result of Goldreich, Goldwasser, and Ron for graph partition problems [O. Goldreich, S. Goldwasser, and D. Ron, J. ACM, 45 (1998), pp. 653-750] and encompasses more recent hypergraph-related results such as the maximal constraint satisfaction approximation of [G. Andersson and L. Engebretsen, Random Structures Algorithms, 21 (2002), pp. 14-32].

/pdf/approximate-hypergraph-partitioning-and-applications-151yke4q3r.pdf

Asaf Shapira

Papers

Randomized greedy: new variants of some classic approximation algorithms

A New Bound for the Brown--Erdős--Sós Problem

A sparse regular approximation lemma

All-Pairs Bottleneck Paths in Vertex Weighted Graphs

Approximate Hypergraph Partitioning and Applications