Computational Network Analysis Research Articles

Computational network analysis of two popular skin cancers provides insights into the molecular mechanisms and reveals common therapeutic targets.

Basal Cell Carcinoma (BCC) and Actinic Keratosis (AK) are prevalent skin conditions with significant health complications. The molecular mechanisms underlying these conditions and their potential shared pathways remain ambiguous despite their prevalence. Therefore, this study aims to elucidate the common molecular pathways and potential therapeutic targets for BCC and AK through comprehensive computational network analysis. Linkage analysis was performed to identify common liable genes between BCC and AK. Protein-protein interactions (PPIs), Topological properties, GO enrichment, pathway enrichment, and gene regulatory network analyses were also performed to reveal potential molecular mechanisms and pathways. Furthermore, we evaluated protein-drug interactions (PDIs) to identify potential therapeutic targets. Our analysis revealed 22 common genes between BCC and AK: TP53, EGFR, CDKN2A, MMP9, PTGS2, VDR, BCL2, MMP2, EZH2, TP63, FOXP3, MSH2, MMP14, FLG, MC1R, CDKN2B, TIMP3, TYR, SOX10, IRF4, KRT17, and NID1. PPI network analysis highlighted TP53 and EGFR as central hubs, validated using RNA-seq data. Co-expression and physical interaction analysis revealed a strong interplay between the common genes at the transcriptional and functional levels. GO analysis identified skin cancer-relevant terms: "skin development", "immune system development", and "response to radiation" as significantly enriched biological processes, while pathway enrichment analysis highlighted several cancer-related pathways enrichment. Gene regulatory network analysis revealed complex interactions between genes, miRNAs, and transcription factors, with TP53, BCL2, and EGFR playing central roles. PDI network analysis identified ibuprofen as a potential therapeutic agent targeting PTGS2 and BCL2, while other proteins VDR, MMP2, MMP9, and TYR showed interactions with multiple drugs. This computational analysis provides valuable insights into the shared molecular mechanisms of BCC and AK, revealing common pathways and potential therapeutic targets for developing novel treatment strategies and repurposing existing drugs for these prevalent skin cancers. Therefore, these findings may guide future research in understanding and developing targeted therapies for both conditions.

Networked corporate advocacy in a polarized public arena: analyzing discourse networks of U.S. Fortune 500 companies on controversial issues

Abstract This study investigates how U.S. Fortune 500 corporate social advocacy (CSA) discourse on multiple contentious issues is associated with public attention on Twitter/X. Our theoretical framework elucidates three discourse types and conceptualizes the diversity, structure, and stability of discourse coalition networks. Utilizing computational methods and dynamic network analysis of 43,791 corporate tweets over two years, findings reveal a prevalent use of aspirational communication in addressing climate change and healthcare disparities. Functional discourse is predominantly employed in tweets on racial equality and COVID-19 vaccination, while localization discourse is concentrated in tweets about gender/sexuality. Network analysis shows that discourse coalitions exist across industries, with a convergence in discourse types and low temporal stability in network compositions. Most discourse types and features of discourse coalition networks are positively associated with public attention to CSA tweets, though these associations vary across different issues.

HybridGNN: A Self-Supervised Graph Neural Network for Efficient Maximum Matching in Bipartite Graphs

Solving maximum matching problems in bipartite graphs is critical in fields such as computational biology and social network analysis. This study introduces HybridGNN, a novel Graph Neural Network model designed to efficiently address complex matching problems at scale. HybridGNN leverages a combination of Graph Attention Networks (GATv2), Graph SAGE (SAGEConv), and Graph Isomorphism Networks (GIN) layers to enhance computational efficiency and model performance. Through extensive ablation experiments, we identify that while the SAGEConv layer demonstrates suboptimal performance in terms of accuracy and F1-score, configurations incorporating GATv2 and GIN layers show significant improvements. Specifically, in six-layer GNN architectures, the combinations of GATv2 and GIN layers with ratios of 4:2 and 5:1 yield superior accuracy and F1-score. Therefore, we name these GNN configurations HybridGNN1 and HybridGNN2. Additionally, techniques such as mixed precision training, gradient accumulation, and Jumping Knowledge networks are integrated to further optimize performance. Evaluations on an email communication dataset reveal that HybridGNNs outperform traditional algorithms such as the Hopcroft–Karp algorithm, the Hungarian algorithm, and the Blossom/Edmonds’ algorithm, particularly for large and complex graphs. These findings highlight HybridGNN’s robust capability to solve maximum matching problems in bipartite graphs, making it a powerful tool for analyzing large-scale and intricate graph data. Furthermore, our study aligns with the goals of the Symmetry and Asymmetry Study in Graph Theory special issue by exploring the role of symmetry in bipartite graph structures. By leveraging GNNs, we address the challenges related to symmetry and asymmetry in graph properties, thereby improving the reliability and fault tolerance of complex networks.

Peer Interaction Dynamics and Second Language Learning Trajectories During Study Abroad: A Longitudinal Investigation Using Dynamic Computational Social Network Analysis

AbstractUsing computational Social Network Analysis (SNA), this longitudinal study investigates the development of the interaction network and its influence on the second language (L2) gains of a complete cohort of 41 U.S. sojourners enrolled in a 3‐month intensive study‐abroad Arabic program in Jordan. Unlike extant research, our study focuses on students’ interactions with alma mater classmates, reconstructing their complete network, tracing the impact of individual students’ positions in the social graph using centrality metrics, and incorporating a developmental perspective with three measurement points. Objective proficiency gains were influenced by predeparture proficiency (negatively), multilingualism, perceived integration of the peer learner group (negatively), and the number of fellow learners speaking to the student. Analyses reveal relatively stable same‐gender cliques, but with changes in the patterns and strength of interaction. We also discuss interesting divergent trajectories of centrality metrics, L2 use, and progress; predictors of self‐perceived progress across skills; and the interplay of context and gender.

Quercetin restores respiratory mucosal barrier dysfunction in Mycoplasma gallisepticum-infected chicks by enhancing Th2 immune response.

Mycoplasma gallisepticum (MG) has long been a pathogenic microorganism threatening the global poultry industry. Previous studies have demonstrated that the mechanism by which quercetin (QUE) inhibits the colonization of MG in chicks differs from that of antibiotics. However, the molecular mechanism by which QUE facilitates the clearance of MG remains unclear. The aim of this study was to investigate the molecular mechanism of MG clearance by QUE, with the expectation of providing new options for the treatment of MG. A model of MG infection in chicks and MG-induced M1 polarization in HD-11 cells were established. The mechanism of QUE clearance of MG was investigated by evaluating the relationship between tracheal mucosal barrier integrity, antibody levels, Th1/Th2 immune balance and macrophage metabolism and M1/M2 polarization balance. Furthermore, network pharmacology and molecular docking techniques were employed to explore the potential molecular pathways connecting QUE, M2 polarization, and fatty acid oxidation (FAO). The findings indicate that QUE remodels tracheal mucosal barrier function by regulating tight junctions and secretory immunoglobulin A (sIgA) expression levels. This process entails the regulatory function of QUE on the Th1/Th2 immune imbalance that is induced by MG infection in the tracheal mucosa. Moreover, QUE intervention impeded the M1 polarization of HD-11 cells induced by MG infection, while simultaneously promoting M2 polarization through the induction of FAO. Conversely, inhibitors of the FAO pathway impede this effect. The results of computer network analysis suggest that QUE may induce FAO via the PI3K/AKT pathway to promote M2 polarization. Notably, inhibition of the PI3K/AKT pathway was found to effectively inhibit M2 polarization in HD-11 cells, while having a limited effect on FAO. QUE promotes M2 polarization of HD-11 cells to enhance Th2 immune response through FAO and PI3K/AKT pathways, thereby restoring tracheal mucosal barrier function and ultimately inhibiting MG colonization.

#SouthAsians4BlackLives: racial positionality in digital allyship and the prospects for cross-racial solidarity for racial justice in the USA

ABSTRACT The presence of multiple racial groups was a key feature of Black Lives Matter (BLM) protests in 2020. This study investigates racial positionalities embedded in expressions of digital allyship with BLM on Instagram by South Asians in the U.S.A. and the prospects for cross-racial solidarity for racial justice. Our computational network analysis of Instagram posts using variations of the hashtag #sa4bl over a six-month period reveals that South Asian activists, influencers and everyday users expressed allyship; however, they formed distinct Instagram communities signalling multiple constructions of allyship. Qualitative analysis indicates two distinct repertoires of allyship; one focussed on consciousness-raising within South Asian communities, and the other on direct expression of allyship with BLM. These repertoires point to the presence of both ethnic and racial positionalities in South Asian digital allyship, reflecting divergent interests based on intersecting identities. Our findings complicate recent evaluations of cross-racial allyship for racial justice amongst South Asian Americans.

Utilising interaction features on Twitter/now X to understand the 'messaging' of migration amongst non-elite users

Different aspects of interactions on social media — communication and action — imply distinctive ways of knowing the social world. I present a new methodological approach that utilizes ‘big’ social media data to understand politically salient issues such as the ‘messaging’ of migration on Twitter/now X. An iterative abductive interpretivist analytical strategy drawing on computational and qualitative social science techniques was applied to a corpus of 47,978 tweets created over five months around the time of lifting of temporary controls on free movement from Romania and Bulgaria to the U.K. in January 2014. Initial computational network analysis on the retweet action feature revealed a small number of highly influential users and a large proportion of isolated users (non-elites) who were never retweeted. Given paucity of understanding of how elite narratives on migration are absorbed, accepted or contested by non-elites, the next stage involved qualitative thematic analysis of a sub-sample of actual tweets (communication) from non-elites to understand meaning-making in views expressed. Qualitative analysis confirmed presence of highly polarised immigration attitudes amongst non-elites but also revealed their values and beliefs about national belonging. These findings prompted questions about what or who influences these values amongst non-elites and whether there are any structural differences in information flows amongst anti- and pro-immigration users. In the third stage, computational surface thematic mapping of different aspects of communication and action in the whole corpus revealed importance of the entire media environment but also differences in the presence or lack of echo-chambers amongst those expressing anti- or pro-immigrant sentiments. This article demonstrates the potential of cross-disciplinary analytical strategies when investigating politically salient issues on social media.

Determining key mammalian species and food web robustness across different land cover vegetation using network analysis

Abstract. Risdiyanto I, Santosa Y, Santoso N, Sunkar A. 2024. Determining key mammalian species and food web robustness across different land cover vegetation using network analysis. Biodiversitas 25: 3175-3190. The ecosystem's food web depicts the intricate energy flow and complex interactions among its diverse organisms. Organisms are not confined to single trophic levels or food chains; they establish multiple food connections with other organisms within the ecosystem. This study aims to identify critical mammalian species and assess the robustness of various ecosystem types, considering the diversity of species presence, through food web analysis. Computational graphic-based network analysis is employed to achieve this goal. Ecosystem types, categorized by their land cover, include plantation/agricultural forests, Bushes and shrubss, forests, and mixed landscapes. Network centrality metrics such as degree, closeness, betweenness, and eigenvector centrality are utilized to evaluate the presence of key species and ecosystem robustness. The relative contribution of mammalian species as connectors and regulators of energy flow in the food web ranges from 8-23% of all nodes involved. Key mammalian species are classified into ecosystem stability and sustainability keys. In Bushes and shrubss ecosystems, key species predominantly consist of mammalian predator species that are crucial for maintaining ecosystem stability through population control. Conversely, in other ecosystems, key species are primarily connectors, ensuring the sustained energy flow. The most resilient food webs are observed in Bushes and shrubss and mixed ecosystems due to their higher biomass growth rates and abundant presence of mammalian species. Utilizing food web analysis can significantly contribute to species and ecosystem conservation efforts by offering a comprehensive understanding of interspecies interactions, food web structures, and ecosystem dynamics.

Unravelling human migrations: DISPERSALS Project sheds light on early Homo sapiens' journey

Unravelling human migrations: DISPERSALS Project sheds light on early Homo sapiens' journey DISPERSALS will compare the human occupation and ecology between central Mozambique and eastern and southern Africa using a multi-scale approach based on the study of regional diachronic cultural traits. It will reconstruct regional population patterns, followed by comparative quantitative population genetics combined with GIS computational network analyses. The results will then be integrated through agent-based modelling, based on the incremental creation, elimination, or reorientation of network links to simulate a quantitative framework to study the evolution of population dispersal across southern-eastern Africa. The project will be crucial in providing groundbreaking high-resolution archaeological, chronological and paleoenvironmental data. DISPERSALS will deliver a fundamental perspective on the key processes that triggered migrations and dispersals within Africa and out of Africa, ultimately resulting in the human diaspora over the entire planet.

Nitisinone attenuates cartilage degeneration and subchondral osteoclastogenesis in osteoarthritis and concomitantly inhibits the cGAS/STING/NF-κB pathway

Osteoarthritis (OA) is a chronic degenerative joint disease characterized by cartilage degeneration and subchondral bone remodelling. Currently, conservative treatment strategies cannot effectively alleviate the progression of OA. In this study, we used computer network analysis to show that Nitisinone (NTBC) is closely related to extracellular matrix degradation in OA and mainly interferes with the TNF-α signaling pathway. NTBC is an orphan drug used to treat hereditary type I tyrosinemia by altering phenylalanine/tyrosine metabolic flow. In this study, we found that NTBC effectively reduced chondrocyte inflammation and extracellular matrix degradation induced by TNF-α. Mechanistically, NTBC inhibited the cGAS/STING signaling pathway and reduced activation of the STING-dependent NF-κB pathway to alleviate inflammation. In addition, NTBC inhibited osteoclastogenesis and delayed the occurrence of subchondral bone remodelling. In mice with ACLT-induced osteoarthritis, intra-articular injection of NTBC significantly reduced cartilage degradation and subchondral bone remodelling. NTBC showed impressive therapeutic efficacy as a potential pharmaceutical intervention for the treatment of OA.

City Image Research: Theoretical Frameworks, Perspectives, and MethodologiesA Citespace Bibliometric Analysis of SSCI Literature from 2013 to 2023

City image research, as a burgeoning paradigm within communication studies, lacks a comprehensive analysis and empirical research on foreign city images. Employing bibliometric methods, this study utilizes CiteSpace to visually analyze research literature on city images published in SSCI journals between 2013 and 2023. It visualizes co-occurrences of keywords, clusters, timeline of main themes, and knowledge maps. The findings reveal that foreign city image research is characterized by diverse perspectives, spanning disciplines such as geography, sociology, and psychology, constructing complex theoretical frameworks. Methodologically, it exhibits depth and complexity, encompassing macro and micro levels, incorporating traditional quantitative methods and integrating new computational methods like computer vision, network analysis, and spatiotemporal analysis. Moreover, numerous studies combine city image analysis with urban planning, marketing, community development, utilizing big data and artificial intelligence technologies for in-depth analysis. This paper summarizes the field and current status of foreign city image research, offering theoretical perspectives and methodological references for domestic studies.

Design and Development of Computer Network and Login Page Using Mirotic Router Universitas Oriental Timor Lorosa'e

The design of the computer network at the Faculty of Engineering, Universitas Oriental Timor Lorosa'e (UNITAL), is established to support the system's design and development, including the Login page on the MikroTik Router. With the right infrastructure, the services used will be more optimal. The current conditions still face challenges with the network design used for the performance of the computer network. This will include an analysis of the network operating system's quality, proposing a network design that conforms to Cisco standards, and using a design plan based on Cisco standards. The research results provide a simulation or design proposal using Winbox software. The purpose of this study is to understand the performance of the computer network at the Faculty of Engineering, Universitas Oriental Timor Lorosa'e, which requires better analysis and network. Therefore, an analysis of the current computer network is necessary, and the design of a new quality system can be used for optimal results. By using a network topology that aligns with Cisco concepts and implementing it on Winbox software, better outcomes are achieved.

Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling.

In medical genetics, each genetic variant is evaluated as an independent entity regarding its clinical importance. However, in most complex diseases, variant combinations in specific gene networks, rather than the presence of a particular single variant, predominates. In the case of complex diseases, disease status can be evaluated by considering the success level of a team of specific variants. We propose a high dimensional modelling based method to analyse all the variants in a gene network together, which we name "Computational Gene Network Analysis" (CoGNA).To evaluate our method, we selected two gene networks, mTOR and TGF- β. For each pathway, we generated 400 control and 400 patient group samples. mTOR and TGF- β pathways contain 31 and 93 genes of varying sizes, respectively. We produced Chaos Game Representation images for each gene sequence to obtain 2-D binary patterns. These patterns were arranged in succession, and a 3-D tensor structure was achieved for each gene network. Features for each data sample were acquired by exploiting Enhanced Multivariance Products Representation to 3-D data. Features were split as training and testing vectors. Training vectors were employed to train a Support Vector Machines classification model. We achieved more than 96% and 99% classification accuracies for mTOR and TGF- β networks, respectively, using a limited amount of training samples.

Will She Win? Gendered Media Coverage of the 2020 Democratic Party Presidential Primaries

In the 2020 Democratic Presidential Primary campaigns, six women, the most in history, announced their candidacy for the presidency. Using computational topic modeling and network analysis of 84,000 newspaper articles (2.2 million paragraphs), we analyze how gender and electability was discussed during the Primaries. Results reveal that male candidates, and especially Joe Biden, were frequently compared with the incumbent, Donald Trump, while coverage of female candidates focused directly on social policy. Trump’s attack on Biden during the impeachment process produced a lasting effect by promoting discourse topics traditionally dominated by men. This crowded out the topics emphasized by women, such as health care and education. Women candidates’ press coverage was also more reactionary than men’s. Although explicit reference to gender as a discourse topic has decreased, which suggests growing gender parity within the political process, gendered policy salience emerges transitively in comparison with the incumbent. Transitive policy salience emerges as a key explanatory variable that articulates the indirect path media coverage can lead to the marginalization of women candidates.

Temporally complex inflammatory networks in an animal model reveal signatures for interstitial cystitis and bladder pain syndrome phenotype.

Interstitial cystitis and bladder pain syndrome (IC/BPS) presents with symptoms of debilitating bladder pain and is typically a diagnosis of exclusion. The cystoscopic detection of Hunner's lesions increases the likelihood of detecting tissue inflammation on bladder biopsy and increases the odds of therapeutic success with anti-inflammatory drugs. However, the identification of this subgroup remains challenging with the current lack of surrogate biomarkers of IC/BPS. On the path towards identifying biomarkers of IC/BPS, we modeled the dynamic evolution of inflammation in an experimental IC/BPS rodent model using computational biological network analysis of inflammatory mediators (cytokines and chemokines) released into urine. The use of biological network analysis allows us to identify urinary proteins that could be drivers of inflammation and could therefore serve as therapeutic targets for the treatment of IC/BPS. Rats subjected to cyclophosphamide (CYP) injection (150 mg/kg) were used as an experimental model for acute IC/BPS (n = 8). Urine from each void was collected from the rats over a 12-h period and was assayed for 13 inflammatory mediators using Luminex™. Time-interval principal component analysis (TI-PCA) and dynamic network analysis (DyNA), two biological network algorithms, were used to identify biomarkers of inflammation characteristic of IC/BPS over time. Compared to vehicle-treated rats, nearly all inflammatory mediators were elevated significantly (p < 0.05) in the urine of CYP treated rats. TI-PCA highlighted that GRO-KC, IL-5, IL-18, and MCP-1 account for the greatest variance in the inflammatory response. At early time points, DyNA indicated a positive correlation between IL-4 and IL-1β and between TNF-α and IL-1β. Analysis of TI-PCA and DyNA at later time points showed the emergence of IL-5, IL-6, and IFNγ as additional key mediators of inflammation. Furthermore, DyNA network complexity rose and fell before peaking at 9.5 h following CYP treatment. This pattern of inflammation may mimic the fluctuating severity of inflammation associated with IC/BPS flares. Computational analysis of inflammation networks in experimental IC/BPS analysis expands on the previously accepted inflammatory signatures of IC by adding IL-5, IL-18, and MCP-1 to the prior studies implicating IL-6 and GRO as IC/BPS biomarkers. This analysis supports a complex evolution of inflammatory networks suggestive of the rise and fall of inflammation characteristic of IC/BPS flares.

“We are snowflakes”: Minor transnationalism and the cultural resilience of slash fanfiction community in China

This article examines the cultural resilience of the Chinese slash fanfiction subculture. Popular among Chinese youth, the writing and reading of slash are subjected to increasingly stringent regulations due to changes in the political environment. Drawing from the theoretical framework of minor transnationalism, the current study situates the development of the Chinese slash community in the context of post-socialist reform and globalization, arguing that slash fanfiction is a form of non-institutional, border-crossing cultural practice that challenges orthodox heteronormativity in nationalistic discourses. Foregrounding the double-edge-sword role of digital platforms, this article dissects the cultural resilience of the Chinese slash community—manifested as various strategies in keeping the viability of their cultural practices, including cultural enclosure, border-crossing platform-switching, and social media activism. This article contributes to the study of subcultures by bringing a transnational perspective that focuses on continuous, resilient cultural practices in negotiations of alternative cultural and political agendas. Methodologically, it also contributes to social media ethnographic research by testing out a comprehensive toolkit that combines close reading with computational text-mining and visual network analysis in the analysis of multimodal social media discourses.

THE EFFECTS STREET-NETWORK CONFIGURATION IN MODELLING WALKABILITY THROUGH SPACE SYNTAX

This study investigates street network connectivity in an informal settlement, Kampong Taman Sari, Bandung. It generally refers to the informal residential area with minimum facilities and urban services. The majority of these settlements depict irregular patterns and narrow paths or alleys. Thus, kampong inhabitants mainly rely on walking, biking, and riding motorcycles to access vital urban features and functions. This purpose study is to examine the potential connectivity not only for accessibility but also for evacuation movement in an informal settlement. This investigation also aims to understand an associative relation between street-network configuration and informal settlement patterns with the probability distribution of pedestrian movement. This study utilizes computational street network analysis through the space syntax method that consists of two distinctive evaluations, such as axial analysis and visual graph analysis. The following result depicts the spatial accessibility, integration, permeability, walkability, and connectivity based on this relation. Experimenting in modelling a walkable kampong in the urban environment will conduce to urban design qualities of the street environment and street network layout for pedestrians or users.

Naturally mutagenic sequence diversity in a human type II topoisomerase

Type II topoisomerases transiently cleave duplex DNA as part of a strand passage mechanism that helps control chromosomal organization and superstructure. Aberrant DNA cleavage can result in genomic instability, and how topoisomerase activity is controlled to prevent unwanted breaks is poorly understood. Using a genetic screen, we identified mutations in the beta isoform of human topoisomerase II (hTOP2β) that render the enzyme hypersensitive to the chemotherapeutic agent etoposide. Several of these variants were unexpectedly found to display hypercleavage behavior in vitro and to be capable of inducing cell lethality in a DNA repair-deficient background; surprisingly, a subset of these mutations were also observed in TOP2B sequences from cancer genome databases. Using molecular dynamics simulations and computational network analyses, we found that many of the mutations obtained from the screen map to interfacial points between structurally coupled elements, and that dynamical modeling could be used to identify other damage-inducing TOP2B alleles present in cancer genome databases. This work establishes that there is an innate link between DNA cleavage predisposition and sensitivity to topoisomerase II poisons, and that certain sequence variants of human type II topoisomerases found in cancer cells can act as DNA-damaging agents. Our findings underscore the potential for hTOP2β to function as a clastogen capable of generating DNA damage that may promote or support cellular transformation.

Dynamic allostery in thrombin-a review.

Thrombin is a serine protease that catalyzes a large number of different reactions including proteolytic cleave of fibrinogen to make the fibrin clot (procoagulant activity), of the protease activated receptors (for cell signaling) and of protein C generating activated protein C (anticoagulant activity). Thrombin has an effector binding site called the anion binding exosite 1 that is allosterically coupled to the active site. In this review, we survey results from thermodynamic characterization of the allosteric coupling as well as hydrogen-deuterium exchange mass spectrometry to reveal which parts of the thrombin structure are changed upon effector binding and/or mutagenesis, and finally NMR spectroscopy to characterize the different timescales of motions elicited by the effectors. We also relate the experimental work to computational network analysis of the thrombin-thrombomodulin complex.

Chemical Reaction Networks Explain Gas Evolution Mechanisms in Mg-Ion Batteries.

Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to characterize. However, such reactions are critical for a range of technological applications. For instance, in metal-ion batteries, spontaneous electrolyte degradation controls electrode passivation and battery cycle life. Here, to improve our ability to elucidate electrochemical reactivity, we for the first time combine computational chemical reaction network (CRN) analysis based on density functional theory (DFT) and differential electrochemical mass spectroscopy (DEMS) to study gas evolution from a model Mg-ion battery electrolyte─magnesium bistriflimide (Mg(TFSI)2) dissolved in diglyme (G2). Automated CRN analysis allows for the facile interpretation of DEMS data, revealing H2O, C2H4, and CH3OH as major products of G2 decomposition. These findings are further explained by identifying elementary mechanisms using DFT. While TFSI- is reactive at Mg electrodes, we find that it does not meaningfully contribute to gas evolution. The combined theoretical-experimental approach developed here provides a means to effectively predict electrolyte decomposition products and pathways when initially unknown.