Graph-based Algorithm Research Articles

An Exact Method for a First-Mile Ridesharing Problem

Motivated by the worldwide development of shared mobility, we investigate a vehicle routing problem with time windows and deadlines called the first-mile ridesharing problem (FMRSP). The FMRSP involves routing a fleet of vehicles, each servicing customers within specific time windows. The FMRSP generalizes the well-known vehicle routing problem with time windows (VRPTW), additionally imposing that each vehicle route arrives at the destination before the earliest deadline associated with the set of customers served by the route. The FMRSP is also related to the VRPTW and release dates, where in addition to time window constraints, a release date is associated with each customer defining the earliest time that the order is available to leave the depot for delivery. For the FMRSP, we present an exact method based on a branch-price-and-cut (BPC) algorithm combining state-of-the-art techniques and an innovative pricing algorithm. The pricing algorithm is based on a bidirectional bucket graph-based labeling algorithm, in which the backward extension of a label is computed in a constant time. Effective dominance rules used to speed up the computation are also described. Extensive computational studies demonstrate that our proposed BPC algorithm can solve optimality-modified Solomon benchmark instances involving up to 100 customers and real-world instances involving up to 290 customers. Funding: This research was supported by the National Natural Science Foundation of China [Grants 71831003, 72171043, 71831006, and 71901180]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0139 .

Semi-supervised Learning Based on Graph Stochastic Co-Training

This article is devoted to the development of a new approach in semi-supervised machine learning. The goal of this article is to analyze the accuracy of the single-view co-training system, based on the use of a modified graph-based stochastic label propagation algorithm for a multiclass classification problem. Graph transformation of data is preceded by feature decomposition, with three algorithms being compared: Singular Value Decomposition, Truncated Singular Value Decomposition, Iterative Primary Component Analysis, Kernel Primary Component Analysis. To improve the accuracy of the proposed method, additional parameter was included in the label propagation algorithm, allowing for the usage of the algorithm in co-training systems. Further performance increases are achieved via optimization of data modification, which is achieved by applying feature decomposition methods and parallelizing the calculation-heavy processes. As examples of practical use were considered solutions to the problem of multiclass classification for standard datasets of the library sklearn and for the real dataset Traffic Signs Preprocessed. Analyses of the results of the implementation of the proposed approach showed improvements in accuracy and of performance solving the multiclass classification problem.

Fast anchor graph preserving projections

The existing graph-based dimensionality reduction algorithms need to learn an adjacency matrix or construct it in advance, therefore the time complexity of the graph-based dimensionality reduction algorithms is not less than O(n2d), where n denotes the number of samples, d denotes the number of dimensions. Moreover, the existing dimensionality reduction algorithms do not consider the cluster information in the original space, resulting in the weakening or even loss of valuable information after dimensionality reduction. To address the above problems, we propose Fast Anchor Graph Preserving Projections (FAGPP), which learns the projection matrix, the anchors and the membership matrix at the same time. Especially, FAGPP has a built-in Principal Component Analysis (PCA) item, which makes our model not only deal with the cluster information of data, but also deal with the global information of data. The time complexity of FAGPP is O(nmd), where m denotes the number of the anchors and m is much less than n. We propose a novel iterative algorithm to solve the proposed model and the convergence of the algorithm is proved theoretically. The experimental results on a large number of high-dimensional benchmark image data sets demonstrate the efficiency of FAGPP. The data sets and the source code are available from https://github.com/511lab/FAGPP.

A graph-based explanatory model for room-based energy efficiency analysis based on BIM data

Introduction: In recent years, the growing interest in building energy consumption and estimation has led to a wealth of energy data and Building Information Modelling (BIM), providing ample opportunities for data-driven algorithms to be widely applied in the building industry. However, despite promising accuracy in data-driven models for building energy estimation, they only consider building elements and their attributes independently and neglect the interconnected relationship of building elements. Also, Current data-driven models lack interpretability and are often treated as black boxes. As a result, the models cannot be fully trusted for engineering without reasoning the underlying mechanisms behind the estimation.Method: This paper emphasizes the potential of graph-based learning algorithms, specifically GraphSAGE, in utilizing the enriched semantic, geometry, and room topology information derived from BIM data. The aim is to identify critical zones within the building based on their energy consumption characteristics. Besides that, the paper proposed a GraphSAGE explainable model by adopting the SHAP with the proposed NE-GraphSAGE prediction model to make more transparency behind the data-driven models.Results and Discussion: Preliminary results demonstrate the potential to improve pre-construction and post-construction steps by identifying critical zones in buildings and identifying the parameters which affected the efficiency of the zones with low energy consumption.

Enhancing model quality and scalability for mining business processes with invisible tasks in non-free choice

At present, business processes are growing rapidly, resulting in various types of activity relationships and big event logs. Discovering invisible tasks and invisible tasks in non-free choice is challenging. α$ mines invisible prime tasks in non-free choice based on pairs of events, so it consumes considerable processing time. In addition, the invisible tasks formation by α $ is limited to skip, switch, and redo conditions. This study proposes a graph-based algorithm named Graph Advanced Invisible Task in Non-free choice (GAITN) to form invisible tasks in non-free choice for stacked branching relationships condition and handle large event logs. GAITN partitions the event log and creates rules for merging the partitions to scale up the volume of discoverable events. Then, GAITN utilises rules of previous graph-based process mining algorithm to visualises branching relationships (XOR, OR, AND) and creates rules of mining invisible tasks in non-free choice based on obtained branching relationships. This study compared the performance of GAITN with that of Graph Invisible Task (GIT), α $, and Fodina and found that GAITN produces process models with better fitness, precision, generalisation, and simplicity measure based on higher number of events. GAITN significantly improves the quality of process model and scalability of process mining algorithm.

GNN-Based Hierarchical Annotation for Analog Circuits

Analog designs consist of multiple hierarchical functional blocks. Each block can be built using one of several design topologies, where the choice of topology is based on circuit performance requirements. A major challenge in automating analog design is in the identification of these functional blocks, which enables the creation of hierarchical netlist representations. This can facilitate a variety of design automation tasks such as circuit layout optimization because the layout is dictated by constraints at each level, such as symmetry requirements, that depend on the topology of the hierarchical block. Traditional graph-based methods find it hard to automatically identify the large number of structural variants of each block. To overcome this limitation, this paper leverages recent advances in graph neural networks (GNNs). A variety of GNN strategies is used to identify netlist elements for circuit functional blocks at higher levels of the design hierarchy, where numerous design variants are possible. At lower levels of hierarchy, where the degrees of freedom in circuit topology are limited, structures are identified using graph-based algorithms. The proposed hierarchical recognition scheme enables the identification of layout constraints such as symmetry and matching, which enable high-quality hierarchical layouts. This method is scalable across a wide range of analog designs. An experimental evaluation shows a high degree of accuracy over a wide range of analog designs, identifying functional blocks such as low-noise amplifiers, operational transconductance amplifiers, mixers, oscillators, and band-pass filters in larger circuits.

Hypergraph-Based Joint Channel and Power Resource Allocation for Cross-Cell M2M Communication in IIoT

Industrial Internet of Things (IIoT) is the leading application scenario of the fifth generation wireless communication systems (5G) and beyond. Non-orthogonal multiple access (NOMA) has become a key technology for 5G due to its high spectrum efficiency. In this paper, a joint channel and power resource allocation problem is investigated for cross-cell IIoT networks with aim of maximizing sum rate of NOMA-based M2M pairs and cellular Machine Devices (cMDs). Since joint channel and power resource allocation problem is an NP-hard problem, the original problem is transformed into a hypergraph model to optimize channel and power resource allocation. Then, a channel allocation algorithm based on hypergraph colouring theory is proposed, and an alternative power allocation algorithm is presented. Next, some properties of hypergraph colouring and complexities are analysed. Finally, simulation results demonstrate that the proposed algorithm outperforms the graph-based algorithm in terms of sum rate, and also improves the spectrum efficiency significantly.

Extensive Review of State-of-the-Art Classification Techniques for Cuneiform Symbol Imaging: Open Issues and Challenges

The cuneiform script reveals some previously unknown aspects of our past. However, reading ancient clay tablets demands a substantial investment of time and persistent practice over a long period of time. As the fourth millennium came to a close, earlier recording methods gave way to the development of writing – the visual representation of spoken language. The first language to be transcribed in written form in Mesopotamia was Sumerian. Predominantly, the earliest tablets originate from the Uruk site in southern Mesopotamia, possibly marking its birthplace. Digitization cuneiform documents is imperative to boost research focused on the ancient Middle East. A few initiatives embarked upon this endeavor around the year 2000. Nonetheless, the digitization process is time-consuming due to the extensive volume of documents, and a dependable (semi) automatic methodology has yet to be developed. Given the antiquity of cuneiform script, recognizing cuneiform signs using real-world applications via two graph-based algorithms, each with complementary runtime characteristics, remains a manual procedure. Translating cuneiform proves to be a daunting task. Only in relatively recent times has grammar been established scientifically, while lexical challenges remain abundant and far from resolved. Furthermore, the majority of the Sumerian tablets have succumbed to the ravages of time, leaving behind only a handful of ancient depictions. Some of these old images have been preserved in a unique collection or in museums worldwide, allowing specialists to easily apply the sign detector to their cuneiform text studies. In this paper, we will discuss the categorization and analysis of clay tablets using a trained cuneiform model, employing artificial intelligence methodologies. Additionally, we will explore the methods employed, highlighting their strengths and weaknesses. Finally, we will propose potential directions for future research.

Ariadne: synthetic long read deconvolution using assembly graphs

Synthetic long read sequencing techniques such as UST’s TELL-Seq and Loop Genomics’ LoopSeq combine 3' barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3' unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.

SWINN: Efficient nearest neighbor search in sliding windows using graphs

Nearest neighbor search (NNS) is one of the main concerns in data stream applications since similarity queries can be used in multiple scenarios. Online NNS is usually performed on a sliding window by lazily scanning every element currently stored in the window. This paper proposes Sliding Window-based Incremental Nearest Neighbors (SWINN), a graph-based online search index algorithm for speeding up NNS in potentially never-ending and dynamic data stream tasks. Our proposal broadens the application of online NNS-based solutions, as even moderately large data buffers become impractical to handle when a naive NNS strategy is selected. SWINN enables efficient handling of large data buffers by using an incremental strategy to build and update a search graph supporting any distance metric. Vertices can be added and removed from the search graph. To keep the graph reliable for search queries, lightweight graph maintenance routines are run. According to experimental results, SWINN is significantly faster than performing a naive complete scan of the data buffer while keeping competitive search recall values. We also apply SWINN to online classification and regression tasks and show that our proposal is effective against popular online machine learning algorithms.

Graph-based learning for automated code checking – Exploring the application of graph neural networks for design review

Although automated code checking (ACC) has been a subject of interest for many years, we have not yet seen significant breakthroughs in the field that may lead to the development of generic, comprehensive tools for ACC. Hard-coded rules are the backbone of all emerging platforms for ACC. These rules require a significant amount of engineering, which often requires manual labor; and the resulting rule sets are strict and difficult to scale to other building models. On the other hand, approaches relying purely on classic machine learning (e.g. SVM) are too coarse and unable to accurately express building information. In our hope to come up with a more scalable solution, we investigate here a novel workflow that relies on graph-based learning algorithms instead of processing rule sets. We illustrate the suggested workflow by checking accessibility requirements in residential houses, which we believe is one of the more promising rule sets that can be checked using graph-based learning methods. The high accuracy of the obtained results is encouraging to continue exploring Graph Neural Networks (GNN) for this type of ACC, yet rule-based and classic ML-based approaches show other advantages as well (rigor and speed, respectively). The main contribution of this work is therefore its identification of meaningful limitations and directions for future research, including alternative graph structures and GNN architectures.

Unlocking the Potential of Kinase Targets in Cancer: Insights from CancerOmicsNet, an AI-Driven Approach to Drug Response Prediction in Cancer.

Deregulated protein kinases are crucial in promoting cancer cell proliferation and driving malignant cell signaling. Although these kinases are essential targets for cancer therapy due to their involvement in cell development and proliferation, only a small part of the human kinome has been targeted by drugs. A comprehensive scoring system is needed to evaluate and prioritize clinically relevant kinases. We recently developed CancerOmicsNet, an artificial intelligence model employing graph-based algorithms to predict the cancer cell response to treatment with kinase inhibitors. The performance of this approach has been evaluated in large-scale benchmarking calculations, followed by the experimental validation of selected predictions against several cancer types. To shed light on the decision-making process of CancerOmicsNet and to better understand the role of each kinase in the model, we employed a customized saliency map with adjustable channel weights. The saliency map, functioning as an explainable AI tool, allows for the analysis of input contributions to the output of a trained deep-learning model and facilitates the identification of essential kinases involved in tumor progression. The comprehensive survey of biomedical literature for essential kinases selected by CancerOmicsNet demonstrated that it could help pinpoint potential druggable targets for further investigation in diverse cancer types.

Visual-Based Simultaneous Localization and Mapping (VSLAM) Techniques for Robots: A Scientific Review

The main problem facing autonomous robots is to navigate in an environment with the ability to determine its location and simultaneously build a map, SLAM technique can formulate this requirement efficiently. In this paper Filter-based, Graph-based, and AI-based Visual-SLAM techniques have been reviewed. The review shows that the first method suffers from high computations when the number of landmarks increases. The Graph-based algorithms are exposed to drift-error problems which cause a delocalization and require optimization. The AI-based vSLAM has the advantage of not-having complicated mathematical models in the algorithm, and it shows an efficient performance in various environments. The reviewed algorithms utilize different cameras including mono, stereo, and RGB-D cameras. The low-cost RGB-D cameras encourage implementation in modern autonomous robots. This work introduces a scientific-based overview of vSLAM to the reader, by explaining all phases of SLAM, the state-of-the-art algorithms, highlighting the strengths and weaknesses of each paradigm.

Liquid flow in heap leaching using the discrete liquid flow model and graph-based void search algorithm

The liquid flow in many heap leaching processes is discontinuous in nature as it occurs either as droplets or rivulets or in combination. Therefore, it cannot be represented correctly using continuum-based models at very low liquid flow rates, such as in heap leaching, or in non-wetting conditions, where liquid flow is discrete in nature. Many researchers have modelled the liquid flow through the heap packing using continuum models. However, this directly contradicts the experimental observations made by various researchers. In the current study, liquid flow is considered discrete in nature as rivulets and droplets with their rupture and merging phenomena. A single vertical layer of particles is considered for the study. A novel void search algorithm is devised to detect the position, shape, and size of the voids in a heap. The liquid flow behaviour is studied in terms of particle size, tortuosity, liquid distribution, breakthrough time, and contact angle. The study shows that heap leaching processes can be modelled more accurately using Discrete Liquid Flow (DLF) theory by avoiding uncertain experimental parameters such as bed permeability. It is found that particle size in the heap structure has significant effect on the liquid-solid contact area, which is an important parameter to determine the efficiency of the leaching process at an industrial scale.

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein–protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

Dual-GCN-based deep clustering with triplet contrast for ScRNA-seq data analysis

Single-cell RNA sequencing (ScRNA-seq) technology reveals gene expression information at the cellular level. The critical tasks in ScRNA-seq data analysis are clustering and dimensionality reduction. Recent deep clustering algorithms are used to optimize the two tasks jointly, and their variations, graph-based deep clustering algorithms, are used to capture and preserve topological information in the process. However, the existing graph-based deep clustering algorithms ignore the distribution information of nodes when constructing cell graphs which leads to incomplete information in the embedding representation; and graph convolutional networks (GCN), which are most commonly used, often suffer from over-smoothing that leads to high sample similarity in the embedding representation and then poor clustering performance. Here, the dual-GCN-based deep clustering with Triplet contrast (scDGDC) is proposed for dimensionality reduction and clustering of scRNA-seq data. Two critical components are dual-GCN-based encoder for capturing more comprehensive topological information and triplet contrast for reducing GCN over-smoothing. The two components improve the dimensionality reduction and clustering performance of scDGDC in terms of information acquisition and model optimization, respectively. The experiments on eight real ScRNA-seq datasets showed that scDGDC achieves excellent performance for both clustering and dimensionality reduction tasks and is high robustness to parameters.

Event-based sampled ECG morphology reconstruction through self-similarity

Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with standard interpolation techniques. In this work, we leverage the self-similarity of the electrocardiogram (ECG) signal to recover missing features in event-based sampled ECG signals, dynamically selecting patient-representative templates together with a novel dynamic time warping algorithm to infer the morphology of event-based sampled heartbeats.Methods: We acquire a set of uniformly sampled heartbeats and use a graph-based clustering algorithm to define representative templates for the patient. Then, for each event-based sampled heartbeat, we select the morphologically nearest template, and we then reconstruct the heartbeat with piece-wise linear deformations of the selected template, according to a novel dynamic time warping algorithm that matches events to template segments.Results: Synthetic tests on a standard normal sinus rhythm dataset, composed of approximately 1.8 million normal heartbeats, show a big leap in performance with respect to standard resampling techniques. In particular (when compared to classic linear resampling), we show an improvement in P-wave detection of up to 10 times, an improvement in T-wave detection of up to three times, and a 30% improvement in the dynamic time warping morphological distance.Conclusion: In this work, we have developed an event-based processing pipeline that leverages signal self-similarity to reconstruct event-based sampled ECG signals. Synthetic tests show clear advantages over classical resampling techniques.

Vertical Fragmentation Impacts in the Performance and Management Database Health Information System (Comparative Study of Fragmentation Vertically Algorithm)

The cost and the speedness of data access on a query depend on the numbers of attributes. In fact the query function will search for data from various attributes in the database table, whereas not all the attributes are needed on the accessed tables. To avoid the not necessary attributes, Vertical Fragmentation methods can be used. The purpose of this research aims to compare the vertical fragmentation of database tables using Bond Energy algorithm and Graph-Based Vertical Partitioning algorithm to create a distributed database. The results of both algorithms will determine which attributes will be avoided. Then, the results of the two algorithms are compared and evaluated using Partition Evaluator to show the amount of the access data costs that are used, so to find which is the better algorithm to be applied. Based on some experimental results, GBVP algorithm showed a better performance with higher Partition Evaluator value respectively at 4,98; 10,41; 17,95; 4,64; and 14,07 as well as the lower value of execution time respectively at 0,003; 0,002; 0,002; 0,002; 0,003 and 0,003.

An efficient semi-supervised balanced cut with hard pairwise constraints and partial labels

With a small scale of prior information, including pairwise constraints and partial labels, semi-supervised clustering algorithms can improve clustering performance by imposing certain restrictions on the clustering results, and have accordingly attracted a lot of attention in recent years. Moreover, a large number of graph-based semi-supervised clustering algorithms have been proposed, since graphs are naturally suited to pairwise constraints. However, most graph-based approaches involve a two-step process and are not scalable to large-scale datasets due to their high time complexity. Additionally, they also encounter a serious problem of constraint violation. To deal with this issue, we propose a new non-parametric model to directly obtain the indicator matrix by minimizing the cut of the graph with a regularization term that corresponds to the balance of the clusters. To simultaneously support the two forms of prior information, our approach restricts the indicator matrix during optimization, while also ensuring that the clusters obtained by our approach do not violate any constraints. The computational complexity of the proposed approach is nearly linear to the data size n. Experiments show that the algorithm proposed in this paper achieves significant improvements in both clustering performance and efficiency.

Classifying RNA Strands with A Novel Graph Representation Based on the Sequence Free Energy

ABSTRACT Ribonucleic acids (RNA) are macromolecules in all living cell, and they are mediators between DNA and protein. Structurally, RNAs are more similar to the DNA. In this paper, we introduce a compact graph representation utilizing the Minimum Free Energy (MFE) of RNA molecules' secondary structure. This representation represents structural components of secondary RNAs as edges of the graphs, and MFE of these components represents their edge weights. The labeling process is used to determine these weights by considering both the MFE of the 2D RNA structures, and the specific settings in the RNA structures. This encoding is used to make the representation more compact by giving a unique graph representation for the secondary structural elements in the graph. Armed with the representation, we apply graph-based algorithms to categorize RNA molecules. We also present the result of the cutting-edge graph-based methods (All Paths Cycle Embeddings (APC), Shortest Paths Kernel/Embedding (SP), and Weisfeiler - Lehman and Optimal Assignment Kernel (WLOA)) on our dataset [1] using this new graph representation. Finally, we compare the results of the graph-based algorithms to a standard bioinformatics algorithm (Needleman-Wunsch) used for DNA and RNA comparison.