A. Polychronopoulou, Jumanah Alshehri, Z. Obradovic
The question of graph similarity or graph distinguishability arises often in natural systems and their analysis over graphical networks. In many domains, graph similarity is used for graph classification, outlier detection or the identification of distinguished interaction patterns. Several methods have been proposed on how to address this topic, but graph comparison still presents many challenges. Recently, information physics has emerged as a promising theoretical foundation for complex networks. In many applications, it has been demonstrated that natural complex systems exhibit features that can be described and interpreted by measures typically applied in quantum mechanical systems. Therefore, a natural starting point for the identification of network similarity measures is information physics and a series of measures of distance for quantum states. In this work, we report experiments on synthetic and real-world data sets, and compare quantum-inspired measures to a series of state-of-the-art and well-established methods of graph distinguishability. We show that quantum-inspired methods satisfy the mathematical and intuitive requirements for graph similarities, while offering high interpretability.
{"title":"Distinguishability of graphs: a case for quantum-inspired measures","authors":"A. Polychronopoulou, Jumanah Alshehri, Z. Obradovic","doi":"10.1145/3487351.3488330","DOIUrl":"https://doi.org/10.1145/3487351.3488330","url":null,"abstract":"The question of graph similarity or graph distinguishability arises often in natural systems and their analysis over graphical networks. In many domains, graph similarity is used for graph classification, outlier detection or the identification of distinguished interaction patterns. Several methods have been proposed on how to address this topic, but graph comparison still presents many challenges. Recently, information physics has emerged as a promising theoretical foundation for complex networks. In many applications, it has been demonstrated that natural complex systems exhibit features that can be described and interpreted by measures typically applied in quantum mechanical systems. Therefore, a natural starting point for the identification of network similarity measures is information physics and a series of measures of distance for quantum states. In this work, we report experiments on synthetic and real-world data sets, and compare quantum-inspired measures to a series of state-of-the-art and well-established methods of graph distinguishability. We show that quantum-inspired methods satisfy the mathematical and intuitive requirements for graph similarities, while offering high interpretability.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123664437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semantic characterization of the Point-of-Interest (POI) plays an important role for modeling location-based social networks and various related applications like POI recommendation, link prediction etc. However, semantic categories are not available for many POIs which makes this characterization difficult. Semantic annotation aims to predict such missing categories of POIs. Existing approaches learn a representation of POIs using graph neural networks to predict semantic categories. However, LBSNs involve complex and higher order mobility dynamics. These higher order relations can be captured effectively by employing hypergraphs. Moreover, visits to POIs can be attributed to various reasons like temporal characteristics, spatial context etc. Hence, we propose a Multi-view Hypergraph Convolution Network (Multi-HGCN) where we learn POI representations by considering multiple hypergraphs across multiple views of the data. We build a comprehensive model to learn the POI representation capturing temporal, spatial and trajectory-based patterns among POIs by employing hypergraphs. We use hypergraph convolution to learn better POI representation by using spectral properties of hypergraph. Experiments conducted on three real-world datasets show that the proposed approach outperforms the state-of-the-art approaches.
{"title":"Multi-view hypergraph convolution network for semantic annotation in LBSNs","authors":"Manisha Dubey, P. K. Srijith, M. Desarkar","doi":"10.1145/3487351.3488341","DOIUrl":"https://doi.org/10.1145/3487351.3488341","url":null,"abstract":"Semantic characterization of the Point-of-Interest (POI) plays an important role for modeling location-based social networks and various related applications like POI recommendation, link prediction etc. However, semantic categories are not available for many POIs which makes this characterization difficult. Semantic annotation aims to predict such missing categories of POIs. Existing approaches learn a representation of POIs using graph neural networks to predict semantic categories. However, LBSNs involve complex and higher order mobility dynamics. These higher order relations can be captured effectively by employing hypergraphs. Moreover, visits to POIs can be attributed to various reasons like temporal characteristics, spatial context etc. Hence, we propose a Multi-view Hypergraph Convolution Network (Multi-HGCN) where we learn POI representations by considering multiple hypergraphs across multiple views of the data. We build a comprehensive model to learn the POI representation capturing temporal, spatial and trajectory-based patterns among POIs by employing hypergraphs. We use hypergraph convolution to learn better POI representation by using spectral properties of hypergraph. Experiments conducted on three real-world datasets show that the proposed approach outperforms the state-of-the-art approaches.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131127579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saket Gurukar, Srinivas Parthasarathy, R. Ramnath, Catherine Calder, Sobhan Moosavi
Learning a vector representation of locations that reflect human mobility patterns is useful for various tasks, including location recommendation, city planning, urban analysis, and even understanding the neighborhood effects on individuals' health and well-being. Existing approaches that model and learn such representations either do not scale or require significant resources to scale. They often need the entire data to be loaded in memory along with the intermediate data representation (typically a co-location graph) and are usually not feasible to execute on low-resource embedding systems such as edge devices. The research question we seek to address in this article is, can one develop efficient federated learning models for location representation learning such that the training and the subsequent updates of the model can occur on edge devices? We present a simple yet novel model called LocationTrails for learning efficient location embeddings to address this question. We show that our proposed model can be trained under the federated learning paradigm and can, therefore, ensure that the model can be trained in a distributed fashion without centralizing locations visited by all users, thereby mitigating some risks to privacy. We evaluate the performance of LocationTrails on five real-world human mobility datasets drawn from two use cases (four of them from driving trajectory data obtained from a national insurance agency; and one of them from a unique study of adolescent mobility patterns in an urban setting). We compare our proposed LocationTrails model against the strong baselines from the network representation learning field. We show the efficacy of LocationTrails in terms of better embedding quality generation, memory consumption, and execution time. To the best of our knowledge, the federated LocationTrails model is the first model that can generate efficient location embeddings without requiring the complete data to be loaded on a central server.
{"title":"LocationTrails: a federated approach to learning location embeddings","authors":"Saket Gurukar, Srinivas Parthasarathy, R. Ramnath, Catherine Calder, Sobhan Moosavi","doi":"10.1145/3487351.3490964","DOIUrl":"https://doi.org/10.1145/3487351.3490964","url":null,"abstract":"Learning a vector representation of locations that reflect human mobility patterns is useful for various tasks, including location recommendation, city planning, urban analysis, and even understanding the neighborhood effects on individuals' health and well-being. Existing approaches that model and learn such representations either do not scale or require significant resources to scale. They often need the entire data to be loaded in memory along with the intermediate data representation (typically a co-location graph) and are usually not feasible to execute on low-resource embedding systems such as edge devices. The research question we seek to address in this article is, can one develop efficient federated learning models for location representation learning such that the training and the subsequent updates of the model can occur on edge devices? We present a simple yet novel model called LocationTrails for learning efficient location embeddings to address this question. We show that our proposed model can be trained under the federated learning paradigm and can, therefore, ensure that the model can be trained in a distributed fashion without centralizing locations visited by all users, thereby mitigating some risks to privacy. We evaluate the performance of LocationTrails on five real-world human mobility datasets drawn from two use cases (four of them from driving trajectory data obtained from a national insurance agency; and one of them from a unique study of adolescent mobility patterns in an urban setting). We compare our proposed LocationTrails model against the strong baselines from the network representation learning field. We show the efficacy of LocationTrails in terms of better embedding quality generation, memory consumption, and execution time. To the best of our knowledge, the federated LocationTrails model is the first model that can generate efficient location embeddings without requiring the complete data to be loaded on a central server.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128713432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We derive two extensions of the celebrated K-means algorithm as a tool for community detection in feature-rich networks. We define a data-recovery criterion additively combining conventional least-squares criteria for approximation of the network link data and the feature data at network nodes by a partition along with its within-cluster "centers". The dimension of the space at which the method operates is the sum of the number of nodes and the number of features, which may be high indeed. To tackle the so-called curse of dimensionality, we may replace the innate Euclidean distance with cosine distance sometimes. We experimentally validate our proposed methods and demonstrate their efficiency by comparing them to most popular approaches.
{"title":"Community detection in feature-rich networks to meet K-means","authors":"S. Shalileh, B. Mirkin","doi":"10.1145/3487351.3488356","DOIUrl":"https://doi.org/10.1145/3487351.3488356","url":null,"abstract":"We derive two extensions of the celebrated K-means algorithm as a tool for community detection in feature-rich networks. We define a data-recovery criterion additively combining conventional least-squares criteria for approximation of the network link data and the feature data at network nodes by a partition along with its within-cluster \"centers\". The dimension of the space at which the method operates is the sum of the number of nodes and the number of features, which may be high indeed. To tackle the so-called curse of dimensionality, we may replace the innate Euclidean distance with cosine distance sometimes. We experimentally validate our proposed methods and demonstrate their efficiency by comparing them to most popular approaches.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121601129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper enhances the interpretation of silence for purposes of truth discovery on social media. Most solutions to fact-finding problems from social media data focus on what users explicitly post. Absence of a post, however, also plays a key role in interpreting veracity of information. In this paper, we focus on (absent links in) the retweet graph. A user might abstain from propagating content for many potential reasons. For example, they might not be aware of the original post; they might find the content uninteresting; or they might doubt content veracity and refrain from propagation (among other reasons). This paper formulates a joint fact-finding and silence interpretation problem, and shows that the joint formulation significantly improves our ability to distinguish true and false claims. An unsupervised algorithm, Joint Network Embedding and Maximum Likelihood (JNEML) framework, is developed to solve this problem. We show that the joint algorithm outperforms other unsupervised baselines significantly on truth discovery tasks on three empirical data sets collected using the Twitter API.
{"title":"The voice of silence: interpreting silence in truth discovery on social media","authors":"H. Cui, T. Abdelzaher","doi":"10.1145/3487351.3488360","DOIUrl":"https://doi.org/10.1145/3487351.3488360","url":null,"abstract":"This paper enhances the interpretation of silence for purposes of truth discovery on social media. Most solutions to fact-finding problems from social media data focus on what users explicitly post. Absence of a post, however, also plays a key role in interpreting veracity of information. In this paper, we focus on (absent links in) the retweet graph. A user might abstain from propagating content for many potential reasons. For example, they might not be aware of the original post; they might find the content uninteresting; or they might doubt content veracity and refrain from propagation (among other reasons). This paper formulates a joint fact-finding and silence interpretation problem, and shows that the joint formulation significantly improves our ability to distinguish true and false claims. An unsupervised algorithm, Joint Network Embedding and Maximum Likelihood (JNEML) framework, is developed to solve this problem. We show that the joint algorithm outperforms other unsupervised baselines significantly on truth discovery tasks on three empirical data sets collected using the Twitter API.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"354 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120931430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liang-yu Chen, Yutong Chen, Young D. Kwon, Youwen Kang, Pan Hui
With the rise of Location-Based Social Networks (LBSNs) and their heavy reliance on User-Generated Content, it has become essential to attract and keep more users, which makes the churn prediction problem interesting. Recent research focuses on solving the task by utilizing complex neural networks. However, due to the black-box nature of those proposed deep learning algorithms, it is still a challenge for LBSN managers to interpret the prediction results and design strategies to prevent churning behavior. Therefore, in this paper, we perform the first investigation into the interpretability of the churn prediction in LBSNs. We proposed a novel attention-based deep learning network, Interpretable Attention Network (IAN), to achieve high performance while ensuring interpretability. The network is capable to process the complex temporal multivariate multidimensional user data from LBSN datasets (i.e. Yelp and Foursquare) and provides meaningful explanations of its prediction. We also utilize several visualization techniques to interpret the prediction results. By analyzing the attention output, researchers can intuitively gain insights into which features dominate the model's prediction of churning users. Finally, we expect our model to become a robust and powerful tool to help LBSN applications to understand and analyze user churning behavior and in turn remain users.
{"title":"IAN: interpretable attention network for churn prediction in LBSNs","authors":"Liang-yu Chen, Yutong Chen, Young D. Kwon, Youwen Kang, Pan Hui","doi":"10.1145/3487351.3488328","DOIUrl":"https://doi.org/10.1145/3487351.3488328","url":null,"abstract":"With the rise of Location-Based Social Networks (LBSNs) and their heavy reliance on User-Generated Content, it has become essential to attract and keep more users, which makes the churn prediction problem interesting. Recent research focuses on solving the task by utilizing complex neural networks. However, due to the black-box nature of those proposed deep learning algorithms, it is still a challenge for LBSN managers to interpret the prediction results and design strategies to prevent churning behavior. Therefore, in this paper, we perform the first investigation into the interpretability of the churn prediction in LBSNs. We proposed a novel attention-based deep learning network, Interpretable Attention Network (IAN), to achieve high performance while ensuring interpretability. The network is capable to process the complex temporal multivariate multidimensional user data from LBSN datasets (i.e. Yelp and Foursquare) and provides meaningful explanations of its prediction. We also utilize several visualization techniques to interpret the prediction results. By analyzing the attention output, researchers can intuitively gain insights into which features dominate the model's prediction of churning users. Finally, we expect our model to become a robust and powerful tool to help LBSN applications to understand and analyze user churning behavior and in turn remain users.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126467588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Medical staff can be considerably supported in patient healthcare delivery thanks to the adoption of machine learning and deep learning methods by enhancing clinicians decisions and analysis with targeted clinical knowledge, patient information, and other health data. This paper proposes a learning methodology that, on the basis of the current patient health status, clinical history, diagnostic and laboratory results, provides insights for clinicians in the diagnosis and therapy decision processes. The approach relies on the concept that patients with similar vital signs patterns are, in all probability, affected by the same or very similar health problems. Thus, they can have the same or very similar diagnoses. Patients physiological signals are modeled as time series and the similarity among them is exploited. The method is formulated as a classification problem in which an ad-hoc multi-label k-nearest neighbor approach is combined with similarity concepts based on word embedding. Experimental results on real-world clinical data have shown that the proposed approach allows detecting diagnoses with a precision up to about 75%.
{"title":"Diagnosis prediction based on similarity of patients physiological parameters","authors":"C. Comito, Deborah Falcone, Agostino Forestiero","doi":"10.1145/3487351.3490962","DOIUrl":"https://doi.org/10.1145/3487351.3490962","url":null,"abstract":"Medical staff can be considerably supported in patient healthcare delivery thanks to the adoption of machine learning and deep learning methods by enhancing clinicians decisions and analysis with targeted clinical knowledge, patient information, and other health data. This paper proposes a learning methodology that, on the basis of the current patient health status, clinical history, diagnostic and laboratory results, provides insights for clinicians in the diagnosis and therapy decision processes. The approach relies on the concept that patients with similar vital signs patterns are, in all probability, affected by the same or very similar health problems. Thus, they can have the same or very similar diagnoses. Patients physiological signals are modeled as time series and the similarity among them is exploited. The method is formulated as a classification problem in which an ad-hoc multi-label k-nearest neighbor approach is combined with similarity concepts based on word embedding. Experimental results on real-world clinical data have shown that the proposed approach allows detecting diagnoses with a precision up to about 75%.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134314071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kumari Neha, Tushar Mohan, Arun Balaji Buduru, P. Kumaraguru
Twitter has emerged as a prominent social media platform for activism and counterpublic narratives. The counterpublics leverage hashtags to build a diverse support network and share content on a global platform that counters the dominant narrative. This paper applies the framework of connective action on the counter-narrative campaign over the cause of death of #SushantSinghRajput. We combine descriptive network, modularity, and hashtag based topical analysis to identify three major mechanisms underlying the campaign: generative role taking, hashtag-based narratives and formation of alignment network towards a common cause. Using the case study of #SushantSinghRajput, we highlight how connective action framework can be used to identify different strategies adopted by counterpublics for the emergence of connective action.
{"title":"Truth and travesty intertwined: a case study of #SSR counterpublic campaign","authors":"Kumari Neha, Tushar Mohan, Arun Balaji Buduru, P. Kumaraguru","doi":"10.1145/3487351.3492717","DOIUrl":"https://doi.org/10.1145/3487351.3492717","url":null,"abstract":"Twitter has emerged as a prominent social media platform for activism and counterpublic narratives. The counterpublics leverage hashtags to build a diverse support network and share content on a global platform that counters the dominant narrative. This paper applies the framework of connective action on the counter-narrative campaign over the cause of death of #SushantSinghRajput. We combine descriptive network, modularity, and hashtag based topical analysis to identify three major mechanisms underlying the campaign: generative role taking, hashtag-based narratives and formation of alignment network towards a common cause. Using the case study of #SushantSinghRajput, we highlight how connective action framework can be used to identify different strategies adopted by counterpublics for the emergence of connective action.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132978887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmad El Shoghri, J. Liebig, R. Jurdak, S. Kanhere
The recent outbreak of coronavirus disease has demonstrated that physical human interactions and modern movement paradigms are the principle drivers for the rapid spatial spread of infectious diseases. Modelling the impact of human mobility is crucial to understand the underlying dynamics of disease spread and consequently to develop effective containment and control strategies. While previous studies have investigated the impact of specific mobility profiles on the spreading dynamics of infectious diseases, they used either highly aggregated spatio-temporal data or portions of datasets that span a short period of time. These limitations do not allow to study how the influence of different mobility aspects on the spread changes as a disease outbreak progresses. In this paper we use large-scale comprehensive human mobility traces to study the impact of the latent period on the spreading dynamics of diseases. In addition, we provide a detailed analysis of how the spreading power of different mobility profiles changes over time. We propose an approach that analyses the behaviour of the individuals' spreading power as time progresses. Through extensive disease spread simulations we uncover a population influence homogeneity threshold, defined by a percentage of the population at which the identified mobility groups become equally influential to the spread.
{"title":"How disease spread dynamics evolve over time","authors":"Ahmad El Shoghri, J. Liebig, R. Jurdak, S. Kanhere","doi":"10.1145/3487351.3488352","DOIUrl":"https://doi.org/10.1145/3487351.3488352","url":null,"abstract":"The recent outbreak of coronavirus disease has demonstrated that physical human interactions and modern movement paradigms are the principle drivers for the rapid spatial spread of infectious diseases. Modelling the impact of human mobility is crucial to understand the underlying dynamics of disease spread and consequently to develop effective containment and control strategies. While previous studies have investigated the impact of specific mobility profiles on the spreading dynamics of infectious diseases, they used either highly aggregated spatio-temporal data or portions of datasets that span a short period of time. These limitations do not allow to study how the influence of different mobility aspects on the spread changes as a disease outbreak progresses. In this paper we use large-scale comprehensive human mobility traces to study the impact of the latent period on the spreading dynamics of diseases. In addition, we provide a detailed analysis of how the spreading power of different mobility profiles changes over time. We propose an approach that analyses the behaviour of the individuals' spreading power as time progresses. Through extensive disease spread simulations we uncover a population influence homogeneity threshold, defined by a percentage of the population at which the identified mobility groups become equally influential to the spread.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123882191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-08DOI: 10.1002/0471740039.vec2016
Demetris Paschalides, G. Pallis, M. Dikaiakos
{"title":"POLAR","authors":"Demetris Paschalides, G. Pallis, M. Dikaiakos","doi":"10.1002/0471740039.vec2016","DOIUrl":"https://doi.org/10.1002/0471740039.vec2016","url":null,"abstract":"","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128641488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：