Expert System for Crop Disease based on Graph Pattern Matching: A proposal

Para la agroindustria, las enfermedades en cultivos constituyen uno de los problemas más frecuentes que generan grandes pérdidas económicas y baja calidad en la producción. Por otro lado, desde las ciencias de la computación, han surgido diferentes herramientas cuya finalidad es mejorar la prevención y el tratamiento de estas enfermedades. En este sentido, investigaciones recientes proponen el desarrollo de sistemas expertos para resolver este problema haciendo uso de técnicas de minería de datos e inteligencia artificial, como inferencia basada en reglas, árboles de decisión, redes bayesianas, entre otras. Además, los grafos pueden ser usados para el almacenamiento de los diferentes tipos de variables que se encuentran presentes en un ambiente de cultivos, permitiendo la aplicación de técnicas de minería de datos en grafos, como el emparejamiento de patrones en los mismos. En este artículo presentamos una visión general de las temáticas mencionadas y una propuesta de un sistema experto para enfermedades en cultivos, basado en emparejamiento de patrones en grafos.For agroindustry, crop diseases constitute one of the most common problems that generate large economic losses and low production quality. On the other hand, from computer science, several tools have emerged in order to improve the prevention and treatment of these diseases. In this sense, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques like rule-based inference, decision trees, Bayesian network, among others. Furthermore, graphs can be used for storage of different types of variables that are present in an environment of crops, allowing the application of graph data mining techniques like graph pattern matching. Therefore, in this paper we present an overview of the above issues and a proposal of an expert system for crop disease based on graph pattern matching

A review of aligners for protein protein interaction networks

Protein Protein Interaction (PPI) can be considered as network. Alignment is the process of mapping nodes from one network to another network. The main objective of network alignment is to identify small, well defined interactome units such as protein complexes or conserved pathways that are analogous in the input network. Network alignment uncovers the relationship between protein complexes and functions. Similarity between two graph structures can be identified by evaluating the topology. Network alignment identifies either topological or sequential similarity. Gene annotations reveal the functional or sequential similarity and it can be evaluated based on semantic similarity. In this paper, we review the various network aligners and classify them according to the methodologies. We discuss the different evaluation metrics and the popular databases of protein interactions

Network Alignment In Heterogeneous Social Networks

Online Social Networks (OSN) have numerous applications and an ever growing user base. This has led to users being a part of multiple social networks at the same time. Identifying a similar user from one social network on another social network will give in- formation about a user’s behavior on different platforms. It further helps in community detection and link prediction tasks. The process of identifying or aligning users in multiple networks is called Network Alignment. More the information we have about the nodes / users better the results of Network Alignment. Unlike other related work in this field that use features like location, timestamp, bag of words, our proposed solution to the Network Alignment problem primarily uses information that is easily available which is the topology of the given network. We look to improve the alignment results by using more information on users like username and profile image features. Experiments are performed to compare the proposed solution in both unsupervised and supervised setting

Addressing Computational Bottlenecks in Higher-Order Graph Matching with Tensor Kronecker Product Structure

Graph matching, also known as network alignment, is the problem of finding a correspondence between the vertices of two separate graphs with strong applications in image correspondence and functional inference in protein networks. One class of successful techniques is based on tensor Kronecker products and tensor eigenvectors. A challenge with these techniques are memory and computational demands that are quadratic (or worse) in terms of problem size. In this manuscript we present and apply a theory of tensor Kronecker products to tensor based graph alignment algorithms to reduce their runtime complexity from quadratic to linear with no appreciable loss of quality. In terms of theory, we show that many matrix Kronecker product identities generalize to straightforward tensor counterparts, which is rare in tensor literature. Improved computation codes for two existing algorithms that utilize this new theory achieve a minimum 10 fold runtime improvement.Comment: 14 pages, 2 pages Supplemental, 5 figure

Network Similarity Decomposition (NSD): A Fast and Scalable Approach to Network Alignment

As graph-structured datasets become commonplace, there is increasing need for efficient ways of analyzing such datasets. These analyses include conservation, alignment, differentiation, and discrimination, among others. When defined on general graphs, these problems are considerably harder than their well-studied counterparts on sets and sequences. This is a direct consequence of the underlying isomorphism associated with many of these problems. In this paper, we study the problem of global alignment of large sparse graphs. Specifically, we investigate efficient methods for computing pairwise topological similarity between nodes in two networks (or within the same network). Pairs of nodes with high similarity can be used to seed global alignments. We present a novel approach to this computationally expensive problem based on un-coupling and decomposing ranking calculations associated with computation of similarity scores. Un-coupling refers independent pre-processing of each input graph. Decomposition implies that pairwise similarity scores can be explicitly broken down into contributions from different link patterns traced back to the initial conditions for the computation. These two concepts result in significant improvements, in terms of computational cost, interpretability of similarity scores, and nature of queries. We show over two orders of magnitude improvement in performance over state-of-the-art IsoRank / Random Walk formulations, and over an order of magnitude over constrained matrix-triple-product formulations, in the context of real datasets