IA Scholar Query: Efficient Densest Subgraph Computation in Evolving Graphs.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgTue, 06 Sep 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Augmenting Structure with Text for Improved Graph Learning
https://scholar.archive.org/work/wgf7mlug2vfmbb5xzc6jdwlooy
Many important problems in machine learning and data mining, such as knowledge base reasoning, personalized entity recommendation, and scientific hypothesis generation, may be framed as learning and inference over a graph data structure. Such problems represent exciting opportunities for advancing graph learning, but also entail significant challenges. Because graphs are typically sparse and defined by a schema, they often do not fully capture the underlying complex relationships in the data. Models that combine graphs with rich auxiliary textual modalities have higher potential for expressiveness, but jointly processing such disparate modalities--that is, sparse structured relations and dense unstructured text--is not straightforward. In this thesis, we consider the important problem of improving graph learning by combining structure and text. The first part of the thesis considers relational knowledge representation and reasoning tasks, demonstrating the great potential of pretrained contextual language models to add renewed depth and richness to graph-structured knowledge bases. The second part of the thesis goes beyond knowledge bases, toward improving graph learning tasks that arise in information retrieval and recommender systems by jointly modeling document interactions and content. Our proposed methodologies consistently improve accuracy over both single-modality and cross-modality baselines, suggesting that, with appropriately chosen inductive biases and careful model design, we can exploit the unique complementary aspects of structure and text to great effect.Tara Safavi, University, Mywork_wgf7mlug2vfmbb5xzc6jdwlooyTue, 06 Sep 2022 00:00:00 GMTAOC; Assembling Overlapping Communities
https://scholar.archive.org/work/zm6shrvfmfdlhhnswkfgrgb4me
Through discovery of meso-scale structures, community detection methods contribute to the understanding of complex networks. To this purpose, a variety of community detection approaches have been developed. Many community finding methods, however, rely on disjoint clustering techniques, in which node membership is restricted to one community or cluster. This strict requirement limits the ability to inclusively describe communities since some nodes may reasonably be assigned to many communities. We have previously reported Iterative K-core Clustering (IKC), a scalable and modular pipeline that discovers disjoint research communities from the scientific literature. We now present Assembling Overlapping Clusters (AOC), a complementary meta-method for overlapping communities as an option that addresses the disjoint clustering problem. We present findings from the use of AOC on a network of over 13 million nodes that captures recent research in the very rapidly growing field of extracellular vesicles in biology.Akhil Jakatdar and Baqiao Liu and Tandy Warnow and George Chackowork_zm6shrvfmfdlhhnswkfgrgb4meFri, 26 Aug 2022 00:00:00 GMTComputational advantage of quantum random sampling
https://scholar.archive.org/work/jotj65lrmjb35figsb6nqkwfoi
Quantum random sampling is the leading proposal for demonstrating a computational advantage of quantum computers over classical computers. Recently, first large-scale implementations of quantum random sampling have arguably surpassed the boundary of what can be simulated on existing classical hardware. In this article, we comprehensively review the theoretical underpinning of quantum random sampling in terms of computational complexity and verifiability, as well as the practical aspects of its experimental implementation using superconducting and photonic devices and its classical simulation. We discuss in detail open questions in the field and provide perspectives for the road ahead, including potential applications of quantum random sampling.Dominik Hangleiter, Jens Eisertwork_jotj65lrmjb35figsb6nqkwfoiThu, 30 Jun 2022 00:00:00 GMTSketch-Based Anomaly Detection in Streaming Graphs
https://scholar.archive.org/work/dpzejo46erbcjolaq2m2eb6z6e
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? For example, in intrusion detection, existing work seeks to detect either anomalous edges or anomalous subgraphs, but not both. In this paper, we first extend the count-min sketch data structure to a higher-order sketch. This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure). We then propose 4 online algorithms that utilize this enhanced data structure, which (a) detect both edge and graph anomalies; (b) process each edge and graph in constant memory and constant update time per newly arriving edge, and; (c) outperform state-of-the-art baselines on 4 real-world datasets. Our method is the first streaming approach that incorporates dense subgraph search to detect graph anomalies in constant memory and time.Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooiwork_dpzejo46erbcjolaq2m2eb6z6eWed, 15 Jun 2022 00:00:00 GMTA Survey on Graph Representation Learning Methods
https://scholar.archive.org/work/wv7b7onubzerndrvz3giy3dznm
Graphs representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification, link prediction and anomaly detection. Many techniques are proposed for generating effective graph representation vectors. Two of the most prevalent categories of graph representation learning are graph embedding methods without using graph neural nets (GNN), which we denote as non-GNN based graph embedding methods, and graph neural nets (GNN) based methods. Non-GNN graph embedding methods are based on techniques such as random walks, temporal point processes and neural network learning methods. GNN-based methods, on the other hand, are the application of deep learning on graph data. In this survey, we provide an overview of these two categories and cover the current state-of-the-art methods for both static and dynamic graphs. Finally, we explore some open and ongoing research directions for future work.Shima Khoshraftar, Aijun Anwork_wv7b7onubzerndrvz3giy3dznmWed, 15 Jun 2022 00:00:00 GMTBilingual dictionary generation and enrichment via graph exploration
https://scholar.archive.org/work/6inqw5242jg45cjpba7hx4dwsi
In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data have stimulated the development and use of openly available linguistic knowledge graphs, as is the case with Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work, we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speed-up, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. Over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as a free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.Shashwat Goel, Jorge Gracia, Mikel Lorenzo Forcadawork_6inqw5242jg45cjpba7hx4dwsiWed, 08 Jun 2022 00:00:00 GMTContributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation
https://scholar.archive.org/work/bgbozvb5y5eejic23g6en4m2va
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as two powerful groups of unsupervised node embedding methods, with various applications to graph-based machine learning problems such as link prediction and community detection. Nonetheless, at the beginning of this Ph.D. project, GAE and VGAE models were also suffering from key limitations, preventing them from being adopted in the industry. In this thesis, we present several contributions to improve these models, with the general aim of facilitating their use to address industrial-level problems involving graph representations. Firstly, we propose two strategies to overcome the scalability issues of previous GAE and VGAE models, permitting to effectively train these models on large graphs with millions of nodes and edges. These strategies leverage graph degeneracy and stochastic subgraph decoding techniques, respectively. Besides, we introduce Gravity-Inspired GAE and VGAE, providing the first extensions of these models for directed graphs, that are ubiquitous in industrial applications. We also consider extensions of GAE and VGAE models for dynamic graphs. Furthermore, we argue that GAE and VGAE models are often unnecessarily complex, and we propose to simplify them by leveraging linear encoders. Lastly, we introduce Modularity-Aware GAE and VGAE to improve community detection on graphs, while jointly preserving good performances on link prediction. In the last part of this thesis, we evaluate our methods on several graphs extracted from the music streaming service Deezer. We put the emphasis on graph-based music recommendation problems. In particular, we show that our methods can improve the detection of communities of similar musical items to recommend to users, that they can effectively rank similar artists in a cold start setting, and that they permit modeling the music genre perception across cultures.Guillaume Salha-Galvanwork_bgbozvb5y5eejic23g6en4m2vaMon, 06 Jun 2022 00:00:00 GMTGraph Neural Networks Designed for Different Graph Types: A Survey
https://scholar.archive.org/work/2dkcr67irfhqlc32me23zhkkk4
Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.Josephine M. Thomas and Alice Moallemy-Oureh and Silvia Beddar-Wiesing and Clara Holzhüterwork_2dkcr67irfhqlc32me23zhkkk4Tue, 24 May 2022 00:00:00 GMTFully Dynamic Four-Vertex Subgraph Counting
https://scholar.archive.org/work/vcxtnnxtobhxzjhglpehrlj24m
This paper presents a comprehensive study of algorithms for maintaining the number of all connected four-vertex subgraphs in a dynamic graph. Specifically, our algorithms maintain the number of paths of length three in deterministic amortized O(m^{1/2}) update time, and any other connected four-vertex subgraph which is not a clique in deterministic amortized update time O(m^{2/3}). Queries can be answered in constant time. We also study the query times for subgraphs containing an arbitrary edge that is supplied only with the query as well as the case where only subgraphs containing a vertex s that is fixed beforehand are considered. For length-3 paths, paws, 4-cycles, and diamonds our bounds match or are not far from (conditional) lower bounds: Based on the OMv conjecture we show that any dynamic algorithm that detects the existence of paws, diamonds, or 4-cycles or that counts length-3 paths takes update time Ω(m^{1/2-δ}). Additionally, for 4-cliques and all connected induced subgraphs, we show a lower bound of Ω(m^{1-δ}) for any small constant δ > 0 for the amortized update time, assuming the static combinatorial 4-clique conjecture holds. This shows that the O(m) algorithm by Eppstein et al. [David Eppstein et al., 2012] for these subgraphs cannot be improved by a polynomial factor.Kathrin Hanauer, Monika Henzinger, Qi Cheng Hua, James Aspnes, Othon Michailwork_vcxtnnxtobhxzjhglpehrlj24mFri, 29 Apr 2022 00:00:00 GMTA New Dynamic Algorithm for Densest Subhypergraphs
https://scholar.archive.org/work/l6mca5gj6becjfi4esuu2iyey4
Computing a dense subgraph is a fundamental problem in graph mining, with a diverse set of applications ranging from electronic commerce to community detection in social networks. In many of these applications, the underlying context is better modelled as a weighted hypergraph that keeps evolving with time. This motivates the problem of maintaining the densest subhypergraph of a weighted hypergraph in a dynamic setting, where the input keeps changing via a sequence of updates (hyperedge insertions/deletions). Previously, the only known algorithm for this problem was due to Hu et al. [19]. This algorithm worked only on unweighted hypergraphs, and had an approximation ratio of (1 + ϵ)r 2 and an update time of O(poly(r, log n)), where r denotes the maximum rank of the input across all the updates. We obtain a new algorithm for this problem, which works even when the input hypergraph is weighted. Our algorithm has a significantly improved (near-optimal) approximation ratio of (1 + ϵ) that is independent of r , and a similar update time of O(poly(r , log n)). It is the first (1 + ϵ)-approximation algorithm even for the special case of weighted simple graphs. To complement our theoretical analysis, we perform experiments with our dynamic algorithm on large-scale, real-world data-sets. Our algorithm significantly outperforms the state of the art [19] both in terms of accuracy and efficiency. CCS CONCEPTS • Theory of computation → Dynamic graph algorithms.Suman K. Bera, Sayan Bhattacharya, Jayesh Choudhari, Prantar Ghoshwork_l6mca5gj6becjfi4esuu2iyey4Mon, 25 Apr 2022 00:00:00 GMTA New Dynamic Algorithm for Densest Subhypergraphs
https://scholar.archive.org/work/wdq54fvaezc7xorruo23tj7gce
Computing a dense subgraph is a fundamental problem in graph mining, with a diverse set of applications ranging from electronic commerce to community detection in social networks. In many of these applications, the underlying context is better modelled as a weighted hypergraph that keeps evolving with time. This motivates the problem of maintaining the densest subhypergraph of a weighted hypergraph in a dynamic setting, where the input keeps changing via a sequence of updates (hyperedge insertions/deletions). Previously, the only known algorithm for this problem was due to Hu et al. [HWC17]. This algorithm worked only on unweighted hypergraphs, and had an approximation ratio of (1+ϵ)r^2 and an update time of O(poly (r, log n)), where r denotes the maximum rank of the input across all the updates. We obtain a new algorithm for this problem, which works even when the input hypergraph is weighted. Our algorithm has a significantly improved (near-optimal) approximation ratio of (1+ϵ) that is independent of r, and a similar update time of O(poly (r, log n)). It is the first (1+ϵ)-approximation algorithm even for the special case of weighted simple graphs. To complement our theoretical analysis, we perform experiments with our dynamic algorithm on large-scale, real-world data-sets. Our algorithm significantly outperforms the state of the art [HWC17] both in terms of accuracy and efficiency.Suman K. Bera, Sayan Bhattacharya, Jayesh Choudhari, Prantar Ghoshwork_wdq54fvaezc7xorruo23tj7gceSun, 17 Apr 2022 00:00:00 GMTMaximizing Convergence Time in Network Averaging Dynamics Subject to Edge Removal
https://scholar.archive.org/work/gfnsdlpvencjpk35epd6dpufhu
We consider the consensus interdiction problem (CIP), in which the goal is to maximize the convergence time of consensus averaging dynamics subject to removing a limited number of network edges. We first show that CIP can be cast as an effective resistance interdiction problem (ERIP), in which the goal is to remove a limited number of network edges to maximize the effective resistance between a source node and a sink node. We show that ERIP is strongly NP-hard, even for bipartite graphs of diameter three with fixed source/sink edges, and establish the same hardness result for the CIP. We then show that both ERIP and CIP cannot be approximated up to a (nearly) polynomial factor assuming exponential time hypothesis. Subsequently, we devise a polynomial-time mn-approximation algorithm for the ERIP that only depends on the number of nodes n and the number of edges m, but is independent of the size of edge resistances. Finally, using a quadratic program formulation for the CIP, we devise an iterative approximation algorithm to find a first-order stationary solution for the CIP and evaluate its good performance through numerical results.S. Rasoul Etesamiwork_gfnsdlpvencjpk35epd6dpufhuMon, 21 Mar 2022 00:00:00 GMTNetwork Analysis of Time Series: Novel Approaches to Network Neuroscience
https://scholar.archive.org/work/4mbsqwpcpbdmpnpph2cy7t7oey
In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.Thomas F Varley, Olaf Spornswork_4mbsqwpcpbdmpnpph2cy7t7oeyFri, 11 Feb 2022 00:00:00 GMTSome Fundamental Theorems in Mathematics
https://scholar.archive.org/work/6lqit72adje3zlo54s5zpgviem
An expository hitchhikers guide to some theorems in mathematics.Oliver Knillwork_6lqit72adje3zlo54s5zpgviemFri, 04 Feb 2022 00:00:00 GMTAdversarially Robust Coloring for Graph Streams
https://scholar.archive.org/work/q55mqpbagjevtcyku6y6vog7xy
A streaming algorithm is considered to be adversarially robust if it provides correct outputs with high probability even when the stream updates are chosen by an adversary who may observe and react to the past outputs of the algorithm. We grow the burgeoning body of work on such algorithms in a new direction by studying robust algorithms for the problem of maintaining a valid vertex coloring of an n-vertex graph given as a stream of edges. Following standard practice, we focus on graphs with maximum degree at most Δ and aim for colorings using a small number f(Δ) of colors. A recent breakthrough (Assadi, Chen, and Khanna; SODA 2019) shows that in the standard, non-robust, streaming setting, (Δ+1)-colorings can be obtained while using only Õ(n) space. Here, we prove that an adversarially robust algorithm running under a similar space bound must spend almost Ω(Δ²) colors and that robust O(Δ)-coloring requires a linear amount of space, namely Ω(nΔ). We in fact obtain a more general lower bound, trading off the space usage against the number of colors used. From a complexity-theoretic standpoint, these lower bounds provide (i) the first significant separation between adversarially robust algorithms and ordinary randomized algorithms for a natural problem on insertion-only streams and (ii) the first significant separation between randomized and deterministic coloring algorithms for graph streams, since deterministic streaming algorithms are automatically robust. We complement our lower bounds with a suite of positive results, giving adversarially robust coloring algorithms using sublinear space. In particular, we can maintain an O(Δ²)-coloring using Õ(n √Δ) space and an O(Δ³)-coloring using Õ(n) space.Amit Chakrabarti, Prantar Ghosh, Manuel Stoeckl, Mark Bravermanwork_q55mqpbagjevtcyku6y6vog7xyTue, 25 Jan 2022 00:00:00 GMTAnomaly Detection in Graphs of Bank Transactions for Anti Money Laundering Applications
https://scholar.archive.org/work/rhaxcp4fvbceroochn5ld257wq
Our aim in this paper is to detect bank clients involved in suspicious activities related to money laundering, using the graph of transactions of the bank. Although we have a labeled real dataset, our target is not only to obtain relevant results on it, but also on random graphs in which typical anomaly patterns have been injected. So, we want simultaneously adequacy to the real data and robustness. Our method is based on designing new features; the most important are those resulting from the reduced egonet, which is the subgraph that remains from an egonet after eliminating the nodes connected with a single edge to the center; another feature is built by appealing to random walks and serves as indicator of circular flows. Our features are added to usual egonet features and a general anomaly detection algorithm, in our case Isolation Forest, serves to detect the anomalies. Experiments on the real data and a comprehensive set of synthetic data show that our approach is adequate, robust and better than some previous methods.Bogdan Dumitrescu, Andra Baltoiu, Stefania Budulanwork_rhaxcp4fvbceroochn5ld257wqCompetition, Cooperation, and People-Centric Operations
https://scholar.archive.org/work/gjxnl7f2kzec3l7d4jeg5y4ecy
This dissertation is concerned with the modeling and analysis of large-scale online platforms, with a focus on the complexities that arise from their multi-agent nature. Specifically, this thesis is interested in two aspects of platform operations: (i) understanding how current models fail to take into account important facets of human behavior and interactions, and leveraging these insights to improve upon state-of-the-art algorithms; and (ii) designing algorithms toward the social good. In broaching the first topic, this thesis illustrates the perils of the strong informational and rationality assumptions typically imposed on human behavior in a variety of settings, and, using tools from online learning and stochastic control, proposes simple and intuitive solutions with strong performance guarantees. In regards to the second area of focus, this work considers how ride-hailing services can be leveraged in conjunction with more traditional transportation options for social welfare maximization objectives, and tackles operational and market design aspects of this problem. In each of these areas of focus, we develop efficient algorithms with provable guarantees that outperform state-ofthe-art methods on real-world datasets.Chamsi Hssainework_gjxnl7f2kzec3l7d4jeg5y4ecyUsing dual-network-analyser for communities detecting in dual networks
https://scholar.archive.org/work/4s2d35tllzavha23qe5nzl745q
Background Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges. Results We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data. Conclusion The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.Pietro Hiram Guzzi, Giuseppe Tradigo, Pierangelo Veltriwork_4s2d35tllzavha23qe5nzl745qMon, 10 Jan 2022 00:00:00 GMTExpander Decomposition and Pruning: Faster, Stronger, and Simpler
https://scholar.archive.org/work/xmynec6eevh6pfqsz6soa4twca
We study the problem of graph clustering where the goal is to partition a graph into clusters, i.e. disjoint subsets of vertices, such that each cluster is well connected internally while sparsely connected to the rest of the graph. In particular, we use a natural bicriteria notion motivated by Kannan, Vempala, and Vetta which we refer to as expander decomposition. Expander decomposition has become one of the building blocks in the design of fast graph algorithms, most notably in the nearly linear time Laplacian solver by Spielman and Teng, and it also has wide applications in practice. We design algorithm for the parametrized version of expander decomposition, where given a graph G of m edges and a parameter ϕ, our algorithm finds a partition of the vertices into clusters such that each cluster induces a subgraph of conductance at least ϕ (i.e. a ϕ expander), and only a O(ϕ) fraction of the edges in G have endpoints across different clusters. Our algorithm runs in O(m/ϕ) time, and is the first nearly linear time algorithm when ϕ is at least 1/log^O(1) m, which is the case in most practical settings and theoretical applications. Previous results either take Ω(m^1+o(1)) time, or attain nearly linear time but with a weaker expansion guarantee where each output cluster is guaranteed to be contained inside some unknown ϕ expander. Our result achieve both nearly linear running time and the strong expander guarantee for clusters. Moreover, a main technique we develop for our result can be applied to obtain a much better expander pruning algorithm, which is the key tool for maintaining an expander decomposition on dynamic graphs. Finally, we note that our algorithm is developed from first principles based on relatively simple and basic techniques, thus making it very likely to be practical.Thatchaphol Saranurak, Di Wangwork_xmynec6eevh6pfqsz6soa4twcaWed, 15 Dec 2021 00:00:00 GMTDense and well-connected subgraph detection in dual networks
https://scholar.archive.org/work/2asa5bapgjgd3dreqf4p7usmrq
Dense subgraph discovery is a fundamental problem in graph mining with a wide range of applications . Despite a large number of applications ranging from computational neuroscience to social network analysis, that take as input a dual graph, namely a pair of graphs on the same set of nodes, dense subgraph discovery methods focus on a single graph input with few notable exceptions . In this work, we focus the following problem: given a pair of graphs G,H on the same set of nodes V, how do we find a subset of nodes S ⊆ V that induces a well-connected subgraph in G and a dense subgraph in H? Our formulation generalizes previous research on dual graphs , by enabling the control of the connectivity constraint on G. We propose a novel mathematical formulation based on k-edge connectivity, and prove that it is solvable exactly in polynomial time. We compare our method to state-of-the-art competitors; we find empirically that ranging the connectivity constraint enables the practitioner to obtain insightful information that is otherwise inaccessible. Finally, we show that our proposed mining tool can be used to better understand how users interact on Twitter, and connectivity aspects of human brain networks with and without Autism Spectrum Disorder (ASD).Tianyi Chen, Francesco Bonchi, David Garcia-Soriano, Atsushi Miyauchi, Charalampos E. Tsourakakiswork_2asa5bapgjgd3dreqf4p7usmrqMon, 06 Dec 2021 00:00:00 GMT