You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We consider the subgraph isomorphism problem where, given two graphs G (source graph) and F (pattern graph), one is to decide whether there is a (not necessarily induced) subgraph of G isomorphic to F. While many practical heuristic algorithms have been developed for the problem, as pointed out by [JAIR 2018], for each of them there are rather small instances which they cannot cope. Therefore, developing an alternative approach that could possibly cope with these hard instances would be of interest. A seminal paper by Alon, Yuster and Zwick [J. ACM 1995] introduced the color coding approach to solve the problem, where the main part is a dynamic programming over color subsets and partial mappings. As with many exponential-time dynamic programming algorithms, the memory requirements constitute the main limiting factor for its usage. Because these requirements grow exponentially with the treewidth of the pattern graph, all existing implementations based on the color coding principle restrict themselves to specific pattern graphs, e.g., paths or trees. In contrast, we provide an efficient implementation of the algorithm significantly reducing its memory requirements so that it can be used for pattern graphs of larger treewidth. Moreover, our implementation not only decides the existence of an isomorphic subgraph, but it also enumerates all such subgraphs (or given number of them). We provide an extensive experimental comparison of our implementation to other available solvers for the problem.

Cited Abstract(s):
[1]: 

[2]: Identifying motifs (or commonly occurring subgraphs templates) has been found to be useful in a number of applications, such as biological and social networks; they have been used to identify building blocks and functional properties, as well as to characterize the underlying networks. Enumerating subgraphs is a challenging computational problem, and all prior results have considered networks with a few thousand nodes. In this paper, we develop a parallel subgraph enumeration algorithm, ParSE, that scales to networks with millions of nodes. Our algorithm is a randomized approximation scheme, that estimates the subgraph frequency to any desired level of accuracy, and allows enumeration of a class of motifs that extends those considered in prior work. Our approach is based on parallelization of an approach called color coding, combined with a stream based partitioning. We also show that ParSE scales well with the number of processors, over a large range.

[3]: We present a new shared-memory parallel algorithm and implementation called FASCIA for the problems of approximate sub graph counting and sub graph enumeration. The problem of sub graph counting refers to determining the frequency of occurrence of a given sub graph (or template) within a large network. This is a key graph analytic with applications in various domains. In bioinformatics, sub graph counting is used to detect and characterize local structure (motifs) in protein interaction networks. Exhaustive enumeration and exact counting is extremely compute-intensive, with running time growing exponentially with the number of vertices in the template. In this work, we apply the color coding technique to determine approximate counts of non-induced occurrences of the sub graph in the original network. Color coding gives a fixed-parameter algorithm for this problem, using a dynamic programming-based counting approach. Our new contributions are a multilevel shared-memory parallelization of the counting scheme and several optimizations to reduce the memory footprint. We show that approximate counts can be obtained for templates with up to 12 vertices, on networks with up to millions of vertices and edges. Prior work on this problem has only considered out-of-core parallelization on distributed platforms. With our new counting scheme, data layout optimizations, and multicore parallelism, we demonstrate a significant speedup over the current state-of-the-art for sub graph counting.

[4]: Protein–protein interaction (PPI) networks of many organisms share global topological features such as degree distribution, k-hop reachability, betweenness and closeness. Yet, some of these networks can differ significantly from the others in terms of local structures: e.g. the number of specific network motifs can vary significantly among PPI networks. Counting the number of network motifs provides a major challenge to compare biomolecular networks. Recently developed algorithms have been able to count the number of induced occurrences of subgraphs with k≤ 7 vertices. Yet no practical algorithm exists for counting non-induced occurrences, or counting subgraphs with k≥ 8 vertices. Counting non-induced occurrences of network motifs is not only challenging but also quite desirable as available PPI networks include several false interactions and miss many others. In this article, we show how to apply the ‘color coding’ technique for counting non-induced occurrences of subgraph topologies in the form of trees and bounded treewidth subgraphs. Our algorithm can count all occurrences of motif G′ with k vertices in a network G with n vertices in time polynomial with n, provided k=O(log n). We use our algorithm to obtain ‘treelet’ distributions for k≤ 10 of available PPI networks of unicellular organisms (Saccharomyces cerevisiae Escherichia coli and Helicobacter Pyloris), which are all quite similar, and a multicellular organism (Caenorhabditis elegans) which is significantly different. Furthermore, the treelet distribution of the unicellular organisms are similar to that obtained by the ‘duplication model’ but are quite different from that of the ‘preferential attachment model’. The treelet distribution is robust w.r.t. sparsification with bait edge coverage of 70 but differences can be observed when bait edge coverage drops to 50 . Contact: cenk@cs.sfu.ca

[5]: Color-coding is a technique to design fixed-parameter algorithms for several NP-complete subgraph isomorphism problems. Somewhat surprisingly, not much work has so far been spent on the actual implementation of algorithms that are based on color-coding, despite the elegance of this technique and its wide range of applicability to practically important problems. This work gives various novel algorithmic improvements for color-coding, both from a worst-case perspective as well as under practical considerations. We apply the resulting implementation to the identification of signaling pathways in protein interaction networks, demonstrating that our improvements speed up the color-coding algorithm by orders of magnitude over previous implementations. This allows more complex and larger structures to be identified in reasonable time; many biologically relevant instances can even be solved in seconds where, previously, hours were required.
There are already some implementations based on the color coding paradigm, where the idea is to randomly color the input graph and search only for its subgraphs, isomorphic to the pattern graph, that are colored in distinct colors (see subs:idea for more detailed description). This approach is used in subgraph counting algorithms, e.g., in ParSE [2] , FASCIA [3] , and in [4] , or in algorithms for path enumeration described in [1] or in [3]7 . Each of these algorithms, after the color coding step, tries to exploit the benefits offered by this technique in its own way; although usually a dynamic programming sees its use. Counting algorithms as ParSE and FASCIA make use of specifically partitioned pattern graphs, which allow to use combinatorial computation. Weighted path enumeration algorithms [1] [3]7 describe a dynamic programming approach and try to optimize it in various ways. However, to the best of our knowledge there is no color coding algorithm capable of enumerating patterns of treewidth larger than 1.