The Maximum Common Subgraph Problem: A Parallel and Multi-Engine Approach

Marcelli, Andrea; Quer, Stefano; Squillero, Giovanni

doi:10.3390/computation8020048

Cited by 8 publications

(4 citation statements)

References 47 publications

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“…The problem of finding a Maximum Common Subgraph (MCS) has been studied for half a century. The problem of finding an MCS is divided into two categories of problems: MCIS (Maximum Common Induced Subgraph) [18, 22, 28] is the problem of finding a common induced subgraph with the largest number of nodes, and MCPS (Maximum Common Partial Subgraph) [3, 23] is the problem of finding a common partial subgraph with the largest number of edges. Another distinction can be made between the connected case and the disconnected case.…”

Section: Related Workmentioning

confidence: 99%

Parallel maximal common subgraphs with labels for molecular biology

Agbeto,

Coti,

Reinharz

2024

Preprint

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Advances in graph algorithmics have allowed in-depth study of many natural objects from molecular biology or chemistry to social networks. Particularly in molecular biology and cheminformatics, understanding complex structures by identifying conserved sub-structures is a key milestone towards the artificial design of novel components with specific functions. Given a dataset of structures, we are interested in identifying all maximum common connected partial subgraphs between each pair of graphs, a task notoriously NP-Hard. In this work, we present 3 parallel algorithms over shared and distributed memory to enumerate all maximal connected common sub-graphs between pairs of arbitrary multi-directed graphs with labels on their edges. We offer an implementation of these methods and evaluate their performance on the non-redundant dataset of all known RNA 3D structures. We show that we can compute the exact results in a reasonable time for each pairwise comparison while taking into account a much more diverse set of interactions---resulting in much denser graphs---resulting in an order of magnitude more conserved modules. All code is available at https://gitlab.info.uqam.ca/cbe/pasigraph and results in the branch results.

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Section: Related Workmentioning

confidence: 99%

Parallel maximal common subgraphs with labels for molecular biology

Agbeto,

Coti,

Reinharz

2024

Preprint

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“…The motivation of using these metrics is that they offer a numerical representation of the graphs and also reduce the data structure repetitions [69].…”

Section: Graph Similaritiesmentioning

confidence: 99%

Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

2021

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The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.

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“…Interestingly, general-purpose computing on GPUs (Graphical Processing Units) is increasingly used to deal with computationally intensive algorithms coming from several domains [11]- [13]. The advent of languages such as OpenCL and CUDA has transformed GPUs into highlyparallel systems which scale gracefully, have a considerable bandwidth, and possess enormous computational power.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Evaluation of Graph Coloring Heuristics on Multi- and Many-Core Architectures

Borione,

Cardone,

Calabrese

et al. 2023

IEEE Access

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Many modern applications are modeled using graphs of some kind. Given a graph, assigning labels (usually called colors) to vertices is called graph coloring. Colors must be assigned so that no two vertices connected by an edge share the same color. Graph coloring has essential applications in many different fields, and many scalable algorithms have been proposed to solve it efficiently, such that researchers have recently started experimenting with coloring, even on many-core GPU devices. In our work, we selected, analyzed, implemented, and compared state-of-the-art algorithms suited for multi-core CPU and many-core GPU architectures. Our analysis allowed us to discover the advantages and disadvantages of each algorithm, and enabled us to implement new strategies for those algorithms running on CPU and GPU devices. We propose a new technique based on "value permutation" and "index shifting" that, once applied to the Jones-Plassmann-Luby algorithm can reduce both the runtime and the number of colors. We compare our code on standard graph benchmarks with the two most used state-of-the-art applications, cuSparse's csrColor and Gunrock's implementations, and one innovative approach named Atos. We present extensive results in terms of computation time and quality of the solution. We show that our fastest implementation is able to achieve high average speedups on mesh-like graphs, with a geometric mean (harmonic mean) of 3.16x (3.05x) against Gunrock, 4.09x (3.06x) against cuSparse, and 4.45x (2.21x) against Atos. Nonetheless it proves to be significantly less effective on scale-free graphs, winning consistently only against Gunrock, with geometric mean (harmonic mean) speedups of 2.76x (2.71x) against Gunrock, 0.13x (0.11x) against cuSparse, and 0.03x (0.01x) against Atos. Moreover, it produces 47% fewer colors than cuSparse, 7% fewer colors than Gunrock, and 63% more colors than Atos.

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The Maximum Common Subgraph Problem: A Parallel and Multi-Engine Approach

Cited by 8 publications

References 47 publications

Parallel maximal common subgraphs with labels for molecular biology

Parallel maximal common subgraphs with labels for molecular biology

Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

An Experimental Evaluation of Graph Coloring Heuristics on Multi- and Many-Core Architectures

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