Discovery Algorithm Research Articles

Stable Heuristic Miner 2: Evaluating the Statistical Stability in Event Logs to Discover Business Processes

Automated process discovery as one of the paradigms of process mining has attracted both industries and academic researchers. These methods offer visibility and comprehension out of complex and unstructured event logs. Over the past decade, the classic heuristic miner and applied heuristic-based process discovery algorithms showed promising results in revealing the hidden process patterns in information systems. One of the challenges related to such algorithms is the arbitrary selection of recorded behaviors in an event log. The offered filtering thresholds are manually adjustable, which could lead to the extraction of a non-optimal process model. This is also visible in commercial process mining solutions. Recently, the first version of the stable heuristic miner algorithm targeted this issue by evaluating the statistical stability of an event log. However, the previous version was limited to evaluating only activities’ behaviors. In this article, we’ll be evaluating the statistical stability of both activities and edges of a graph, which could be discovered from an event log. As a contribution, the stable heuristic miner 2 is introduced. Consequently, the definition of the descriptive reference process model has improved. The novel algorithm is evaluated by using two real-world event logs. These event logs are the familiar Sepsis data set and the urology department patients’ pathways event log, which is recorded by monitoring the interpreted location data of patients on hospital premises and is shared with the scientific community in this article.

Discovering Functional Dependencies through Hitting Set Enumeration

Functional dependencies (FDs) are among the most important integrity constraints in databases. They serve to normalize datasets and thus resolve redundancies, they contribute to query optimization, and they are frequently used to guide data cleaning efforts. Because the FDs of a particular dataset are usually unknown, automatic profiling algorithms are needed to discover them. These algorithms have made considerable advances in the past few years, but they still require a significant amount of time and memory to process datasets of practically relevant sizes. We present FDHits, a novel FD discovery algorithm that finds all valid, minimal FDs in a given relational dataset. FDHits is based on several discovery optimizations that include a hybrid validation approach, effective hitting set enumeration techniques, one-pass candidate validations, and parallelization. Our experiments show that FDHits, even without parallel execution, has a median speedup of 8.1 compared to state-of-the-art FD discovery algorithms while using significantly less memory. This allows the discovery of all FDs even on datasets that could not be processed by the current state-of-the-art.

Large-scale analysis of the N-terminal regulatory elements of the kinase domain in plant Receptor-like kinase family

BackgroundThe N-terminal regulatory element (NRE) of Receptor-like kinases (RLKs), consisting of the juxtamembrane segment in receptor kinases (RKs) and the N-terminal extension segment in RLCKs, is a crucial component that regulates the activities of these proteins. However, the features and functions of the NRE have remained largely unexplored. Herein, we comprehensively analyze 510,233 NRE sequences in RLKs from 528 plant species, using information theory and data mining techniques to unravel their common characteristics and diversity. We also use recombinant RKs to investigate the function of the NRE in vitro.ResultsOur findings indicate that the majority of NRE segments are around 40–80 amino acids in length and feature a serine-rich region and a 14-amino-acid consensus sequence, ‘FSYEELEKAT[D/N]NF[S/D]’, which contains a characteristic α-helix and ST motif that connects to the core kinase domain. This conserved signature sequence is capable of suppressing FERONIA’s kinase activity. A motif discovery algorithm identifies 29 motifs with highly conserved phosphorylation sites in RK and RLCK classes, especially the motif ‘VGPWKpTGLpSGQLQKAFVTGVP’ in LRR-VI-2 class. Phosphorylation of an NRE motif in an LRR-VI-2 member, MDIS1, modulates the auto-phosphorylation of its co-receptor, MIK1, indicating the potential role of NRE as a ‘kinase switch’ in RLK activation. Furthermore, the characterization of phosphorylatable NRE motifs improves the accuracy of predicting phosphorylatable sites.ConclusionsOur study provides a comprehensive dataset to investigate NRE segments from individual RLKs and enhances our understanding of the underlying mechanisms of RLK signal transduction and kinase activation processes in plant adaptation.

Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering

Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification.

Carbon emission causal discovery and multi-step forecasting for global cities

The increasing threat of global climate change is primarily caused by rising carbon emissions, with cities acting as significant contributors. This study bridges two vital gaps in urban carbon neutrality research: unraveling the causal dynamics of carbon emissions within urban networks and forecasting emission trends. This study proposes a reinforcement learning-based causal discovery algorithm, progressively deciphering the complex causal relationships in global urban emissions, and facilitating the creation of directed acyclic causal graphs. Furthermore, a hyperbolic graph neural network-based forecasting algorithm is introduced, through integrated fusion curvature to improve the information transfer between cities, for predicting global urban emission trends. A comparative analysis positions these innovative algorithms against leading methods, using emission data from thousands of cities for predictions one, five, and ten steps ahead. The experiment employs prediction error metrics, Taylor statistics, the Diebold-Mariano test, and the ablation analysis for validation. Results reveal proposed causal discovery algorithm effectively identifies the causality of carbon emissions among cities, while the forecasting algorithm leads other competing models across all prediction ranges. Based on the effectiveness of the algorithms, this study decodes the significant nature of the global urban carbon emission network, offering policy insights for collaborative carbon mitigation in cities worldwide.

Accurate and sensitive mutational signature analysis with MuSiCal

Mutational signature analysis is a recent computational approach for interpreting somatic mutations in the genome. Its application to cancer data has enhanced our understanding of mutational forces driving tumorigenesis and demonstrated its potential to inform prognosis and treatment decisions. However, methodological challenges remain for discovering new signatures and assigning proper weights to existing signatures, thereby hindering broader clinical applications. Here we present Mutational Signature Calculator (MuSiCal), a rigorous analytical framework with algorithms that solve major problems in the standard workflow. Our simulation studies demonstrate that MuSiCal outperforms state-of-the-art algorithms for both signature discovery and assignment. By reanalyzing more than 2,700 cancer genomes, we provide an improved catalog of signatures and their assignments, discover nine indel signatures absent in the current catalog, resolve long-standing issues with the ambiguous ‘flat’ signatures and give insights into signatures with unknown etiologies. We expect MuSiCal and the improved catalog to be a step towards establishing best practices for mutational signature analysis.

LeCaSiM: Learning Causal Structure via Inverse of M-Matrices with Adjustable Coefficients

The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings.

Causal discovery to understand hot corrosion

AbstractGas turbine superalloys experience hot corrosion, driven by factors including corrosive deposit flux, temperature, gas composition, and component material. The full mechanism still needs clarification and research often focuses on laboratory work. As such, there is interest in causal discovery to confirm the significance of factors and identify potential missing causal relationships or codependencies between these factors. The causal discovery algorithm fast causal inference (FCI) has been trialled on a small set of laboratory data, with the outputs evaluated for their significance to corrosion propagation, and compared to existing mechanistic understanding. FCI identified salt deposition flux as the most influential corrosion variable for this limited data set. However, HCl was the second most influential for pitting regions, compared to temperature for more uniformly corroding regions. Thus, FCI generated causal links aligned with literature from a randomised corrosion data set, while also identifying the presence of two different degradation modes in operation.

Response of Vegetation to Drought in the Source Region of the Yangtze and Yellow Rivers Based on Causal Analysis

The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought–vegetation interaction in this area under the influence of climate–permafrost coupling. In this study, based on the saturated vapor pressure deficit (VPD) and soil moisture (SM) that characterize atmospheric and soil drought, as well as the Normalized Differential Vegetation Index (NDVI) and solar-induced fluorescence (SIF) that characterize vegetation greenness and function, the evolution of regional vegetation productivity and drought were systematically identified. On this basis, the technical advantages of the causal discovery algorithm Peter–Clark Momentary Conditional Independence (PCMCI) were applied to distinguish the response of vegetation to VPD and SM. Furthermore, this study delves into the response mechanisms of NDVI and SIF to atmospheric and soil drought, considering different vegetation types and permafrost degradation areas. The findings indicated that low SM and high VPD were the limiting factors for vegetation growth. The positive and negative causal effects of VPD on NDVI accounted for 47.88% and 52.12% of the total area, respectively. Shrubs were the most sensitive to SM, and the response speed of grassland to SM was faster than that of forest land. The impact of SM on vegetation in the SRYY was stronger than that of VPD, and the effect in the frozen soil degradation area was more obvious. The average causal effects of NDVI and SIF on SM in the frozen soil degradation area were 0.21 and 0.41, respectively, which were twice as high as those in the whole area, and SM dominated NDVI (SIF) changes in 62.87% (76.60%) of the frozen soil degradation area. The research results can provide important scientific basis and theoretical support for the scientific assessment and adaptation of permafrost, vegetation, and climate change in the source area and provide reference for ecological protection in permafrost regions.

Label propagation algorithm for community discovery based on centrality and common neighbours

Label propagation algorithm for community discovery based on centrality and common neighbours

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

Handshake mechanism selection in MANETs with directional antennas: A new evaluation view

AbstractHandshake mechanism plays a significant role in neighbour discovery (ND) algorithms in mobile ad hoc networks (MANETs) with directional antennas. In this letter, aiming at 1) the MANET scenario of uniformly‐distributed nodes and 2) the random beam‐scanning manner, the upper and lower bounds of ND time of the network are theoretically derived for both 1‐way and 2‐way. This analysis indicates that the ND time is approximately linear growth with NDT‐Mutual, where the NDT‐Mutual is defined as the expected time for a node to complete mutual discovery with its neighbours. Then the theoretical expression for NDT‐mutual is derived, which is a function of the number of nodes. The asymptotic analysis reveals that when the number of nodes is very small, the NDT‐mutual of 2‐way is lower than 1‐way, while 1‐way performs better when the number of nodes approaches infinity. The number of nodes when 1‐way and 2‐way perform as well is further presented, which can be utilized to guide the selection of handshake mechanism in MANETs with directional antennas. Simulation results demonstrate the accuracy of our modelling for ND and the effectiveness of using NDT‐mutual to approximate who performs better with 1‐way and 2‐way.

Causality-Based PCA Methods for Condition Modeling of Mechatronic Systems

This paper investigates the feature dimensionality reduction problem of high-dimensional data in condition modelling of complex mechatronic systems, aiming at improving performance, robustness and interpretability of classical principle component analysis (PCA) methods. First, the causal discovery algorithms are adopted to construct the corresponding causal network among monitoring variables. Then, two causality-based PCA methods are proposed, based on the corresponding qualitative reachability matrix and quantitative reachability strength matrix, respectively. Average treatment effect (ATE) values are considered as the quantitative measure of the causalities in a reachability strength matrix. The robustness and effectiveness of the proposed methods are verified on a synthetic dataset and four public datasets in comparison with two benchmark methods, i.e. PCA and autoencoder (AE). The proposed methods are also adopted in a high-speed train braking system for extracting informative features from the monitoring variables.

Capturing More Associations by Referencing External Graphs

This paper studies association rule discovery in a graph G 1 by referencing an external graph G 2 with overlapping information. The objective is to enrich G 1 with relevant properties and links from G 2 . As a testbed, we consider Graph Association Rules (GARs). We propose a notion of graph joins to enrich G 1 by aligning entities across G 1 and G 2 . We also introduce a graph filtering method to support graph joins, by fetching only the data of G 2 that pertains to the entities of G 1 , to reduce noise and the size of the fused data. Based on these we develop a parallel algorithm to discover GARs across G 1 and G 2 . Moreover, we provide an incremental GAR discovery algorithm in response to updates to G 1 and G 2 . We show that both algorithms guarantee to reduce parallel runtime when given more processors. Better yet, the incremental algorithm is bounded relative to the batch one. Using real-life and synthetic data, we empirically verify that the methods improve the accuracy of association analyses by 30.4% on average, and scale well with large graphs.

Cover Image

The cover image is based on the Original Article Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm by Lei Pang et al., https://doi.org/10.1049/syb2.12083image

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization

BackgroundIn cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species.ResultsWe introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein–protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions.We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset.ConclusionsOur proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

Meet in the air: Distributed neighbor discovery in 3D networks with directional transceivers

We formulate and analyze a generic neighbor discovery problem in three-dimensional wireless networks with directional transceivers, where any pair of neighbor nodes in 3D Euclidean space need to steer their transceivers towards each other simultaneously to discover each other. This problem, termed as 3D directional neighbor discovery, arises in a variety of emerging networked systems such as flying ad hoc networks composed of drones and distributed space systems composed of spacecrafts. Compared to the omni-directional 2D neighbor discovery problem extensively investigated in the literature, the 3D directional neighbor discovery problem is intuitively more challenging. Motivated by this observation, we establish an algorithmic framework on the 3D directional neighbor discovery problem. We first mathematically formulate the problem and derive the worst-case discovery delay of any neighbor discovery algorithm. Guided by the performance limit, we then design distributed neighbor discovery algorithms achieving bounded and minimal worst-case discovery delay. We further demonstrate how our algorithmic framework can be generalized to solve generic multi-dimensional rendezvous problems. Extensive numerical analysis is then presented to empirically evaluate of our neighbor discovery algorithms.

Incorporating Holding Costs in Continuous-Time Service Network Design: New Model, Relaxation, and Exact Algorithm

The continuous-time service network design problem (CTSNDP) occurs widely in practice. It aims to minimize the total operational cost by optimizing the schedules of transportation services and the routes of shipments for dispatching, which can occur at any time point along a continuous planning horizon. In order to be cost-effective, shipments often wait to be consolidated, which incurs a holding cost. Despite its importance, the holding cost has not been taken into account in existing exact solution methods for the CTSNDP because introducing it significantly complicates the problem and makes solution development very challenging. To tackle this challenge, we develop a new dynamic discretization discovery algorithm, which can solve the CTSNDP with holding cost to exactly optimum. The algorithm is based on a novel relaxation model and several new optimization techniques. Results from extensive computational experiments validate the efficiency and effectiveness of the new algorithm and also demonstrate the benefits that can be gained by taking into account holding costs in solving the CTSNDP. In particular, we show that the significance of the benefits depends on the connectivity of the underlying physical network and the flexibility of the shipments’ time requirements. Funding: This work was partially supported by National Natural Science Foundation of China [Grant 71831008] and the Hong Kong Polytechnic University [Project P0043872]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0104 .

Secure and Lightweight Blockchain-based Truthful Data Trading for Real-Time Vehicular Crowdsensing

As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations.

Model-data matching method for natural disaster emergency service scenarios: implementation based on a knowledge graph and community discovery algorithm

Model-data matching method for natural disaster emergency service scenarios: implementation based on a knowledge graph and community discovery algorithm