Influence maximization in social networks has been intensively studied in recent years, where the goal is to find a small set of seed nodes in a social network that maximizes the spread of influence according to a diffusion model. Recent research on influence maximization mainly focuses on incorporating either user opinions or competitive settings in the influence diffusion model. In many real-world applications, however, the influence diffusion process often involves both real-valued opinions from users and multiple parties that are competing with each other. In this paper, we study the problem of competitive opinion maximization, where the game of influence diffusion includes multiple competing products and the goal is to maximize the total opinions of activated users by each product. This problem is very challenging because it is #P-hard and no longer keeps the property of submodularity. We propose a novel model, called ICOM (Iterative Competitive Opinion Maximization), that can effectively and efficiently maximize the total opinions in competitive games by taking user opinions as well as the competitor's strategy into account. Different from existing influence maximization methods, we inhibit the spread of negative opinions and search for the optimal response to opponents' choices of seed nodes. We apply iterative inference based on a greedy algorithm to reduce the computational complexity. Empirical studies on real-world datasets demonstrate that comparing with several baseline methods, our approach can effectively and efficiently improve the total opinions achieved by the promoted product in the competitive network.
{"title":"Competitive Opinion Maximization in Social Networks","authors":"Jianjun Luo, Xinyue Liu, Xiangnan Kong","doi":"10.1145/3341161.3342899","DOIUrl":"https://doi.org/10.1145/3341161.3342899","url":null,"abstract":"Influence maximization in social networks has been intensively studied in recent years, where the goal is to find a small set of seed nodes in a social network that maximizes the spread of influence according to a diffusion model. Recent research on influence maximization mainly focuses on incorporating either user opinions or competitive settings in the influence diffusion model. In many real-world applications, however, the influence diffusion process often involves both real-valued opinions from users and multiple parties that are competing with each other. In this paper, we study the problem of competitive opinion maximization, where the game of influence diffusion includes multiple competing products and the goal is to maximize the total opinions of activated users by each product. This problem is very challenging because it is #P-hard and no longer keeps the property of submodularity. We propose a novel model, called ICOM (Iterative Competitive Opinion Maximization), that can effectively and efficiently maximize the total opinions in competitive games by taking user opinions as well as the competitor's strategy into account. Different from existing influence maximization methods, we inhibit the spread of negative opinions and search for the optimal response to opponents' choices of seed nodes. We apply iterative inference based on a greedy algorithm to reduce the computational complexity. Empirical studies on real-world datasets demonstrate that comparing with several baseline methods, our approach can effectively and efficiently improve the total opinions achieved by the promoted product in the competitive network.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124848899","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}
Konstantinos F. Xylogiannopoulos, P. Karampelas, R. Alhajj
In recent years, there are very frequent reports of disasters attributed to the climate change and there are several reports that these extreme phenomena will further affect people not only as weather disasters but also indirectly with the shortage of natural resources such as water or food due to the climate change. Towards this direction, there is an on-going research that studies weather phenomena by collecting data not only in the surface of the globe but also at the different levels of the atmosphere. Having such a large volume of data, traditional numerical weather prediction models may not be able to assimilate those data and extract knowledge useful for the prediction of extreme phenomena. Thus, analysis of weather data has been transformed into a big data analytics problem which may enable weather scientists to better understand the interrelations of the weather variables and use the knowledge discovered to improve their prediction models. In this context, the current paper proposes a big data analytics methodology that is able to detect all common patterns between different weather variables in neighboring or distant points in a specific time window revealing useful associations between weather variables which is not possible to detect otherwise with the traditional numerical methods. The proposed methodology is based on a data structure that is able to store the magnitude of the weather data in different dimensions and a pattern detection algorithm which is able to detect all common patterns. The experimental results using weather data from the National Oceanic and Atmospheric Administration (NOAA) revealed interesting otherwise unknown patterns in two weather variables for two specific locations that were studied.
{"title":"Multivariate Motif Detection in Local Weather Big Data","authors":"Konstantinos F. Xylogiannopoulos, P. Karampelas, R. Alhajj","doi":"10.1145/3341161.3343518","DOIUrl":"https://doi.org/10.1145/3341161.3343518","url":null,"abstract":"In recent years, there are very frequent reports of disasters attributed to the climate change and there are several reports that these extreme phenomena will further affect people not only as weather disasters but also indirectly with the shortage of natural resources such as water or food due to the climate change. Towards this direction, there is an on-going research that studies weather phenomena by collecting data not only in the surface of the globe but also at the different levels of the atmosphere. Having such a large volume of data, traditional numerical weather prediction models may not be able to assimilate those data and extract knowledge useful for the prediction of extreme phenomena. Thus, analysis of weather data has been transformed into a big data analytics problem which may enable weather scientists to better understand the interrelations of the weather variables and use the knowledge discovered to improve their prediction models. In this context, the current paper proposes a big data analytics methodology that is able to detect all common patterns between different weather variables in neighboring or distant points in a specific time window revealing useful associations between weather variables which is not possible to detect otherwise with the traditional numerical methods. The proposed methodology is based on a data structure that is able to store the magnitude of the weather data in different dimensions and a pattern detection algorithm which is able to detect all common patterns. The experimental results using weather data from the National Oceanic and Atmospheric Administration (NOAA) revealed interesting otherwise unknown patterns in two weather variables for two specific locations that were studied.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125434279","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}
Software for electronic prescriptions offers healthcare providers numerous benefits including increased patient safety, prescribing effectiveness, workflow efficiencies, and financial savings. Particularly due to recent legislation, an increasing number of organizations are adopting this software. Yet, there are many concerns and risks associated with e-prescription technologies. Despite increased prescribing effectiveness, there is still the potential for human error. Many software concerns including integration and the implementation of specific transactions like CancelRx still need to be addressed. Additionally, some patients have negative perceptions of e-Rx that must be overcome. To this effect, this research conducts risk assessment of two real world cases (the CVS Pharmacy (retail setting) and the Ascension Seton (inpatient setting)) using the NIST risk model. The study concludes that the top three challenges in the pharmacy domain are comprised of a lack in the implementation of essential software functionalities, the loss and theft of portable devices, and errors due to email phishing attacks.
{"title":"Risk Assessment of Pharmacies & Electronic Prescriptions","authors":"Michelle Bowman, Subrata Acharya","doi":"10.1145/3341161.3343697","DOIUrl":"https://doi.org/10.1145/3341161.3343697","url":null,"abstract":"Software for electronic prescriptions offers healthcare providers numerous benefits including increased patient safety, prescribing effectiveness, workflow efficiencies, and financial savings. Particularly due to recent legislation, an increasing number of organizations are adopting this software. Yet, there are many concerns and risks associated with e-prescription technologies. Despite increased prescribing effectiveness, there is still the potential for human error. Many software concerns including integration and the implementation of specific transactions like CancelRx still need to be addressed. Additionally, some patients have negative perceptions of e-Rx that must be overcome. To this effect, this research conducts risk assessment of two real world cases (the CVS Pharmacy (retail setting) and the Ascension Seton (inpatient setting)) using the NIST risk model. The study concludes that the top three challenges in the pharmacy domain are comprised of a lack in the implementation of essential software functionalities, the loss and theft of portable devices, and errors due to email phishing attacks.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122398636","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}
Vanessa Cedeno-Mieles, Zhihao Hu, Xinwei Deng, Yihui Ren, Abhijin Adiga, C. Barrett, S. Ekanayake, Gizem Korkmaz, C. Kuhlman, D. Machi, M. Marathe, S. Ravi, Brian J. Goode, Naren Ramakrishnan, Parang Saraf, Nathan Self, N. Contractor, J. Epstein, M. Macy
In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.
{"title":"Mechanistic and Data-Driven Agent-Based Models to Explain Human Behavior in Online Networked Group Anagram Games","authors":"Vanessa Cedeno-Mieles, Zhihao Hu, Xinwei Deng, Yihui Ren, Abhijin Adiga, C. Barrett, S. Ekanayake, Gizem Korkmaz, C. Kuhlman, D. Machi, M. Marathe, S. Ravi, Brian J. Goode, Naren Ramakrishnan, Parang Saraf, Nathan Self, N. Contractor, J. Epstein, M. Macy","doi":"10.1145/3341161.3342965","DOIUrl":"https://doi.org/10.1145/3341161.3342965","url":null,"abstract":"In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995454","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}
Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on several key assumptions about the behavior of geotagged and non-geotagged users. However, these assumptions have not been fully validated. Lack of understanding the geotagging behavior prohibits people further utilizing it. In this paper, we present an empirical study of geotagging behavior on Twitter based on more than 40 billion tweets collected from 20 million users. There are three main findings that may challenge these common assumptions. Firstly, different groups of users have different geotagging preferences. For example, less than 3% of users speaking in Korean are geotagged, while more than 40% of users speaking in Indonesian use geotags. Secondly, users who report their locations in profiles are more likely to use geotags, which may affects the generability of those location prediction systems on non-geotagged users. Thirdly, strong homophily effect exists in users' geotagging behavior, that users tend to connect to friends with similar geotagging preferences.
{"title":"A Large-Scale Empirical Study of Geotagging Behavior on Twitter","authors":"Binxuan Huang, Kathleen M. Carley","doi":"10.1145/3341161.3342870","DOIUrl":"https://doi.org/10.1145/3341161.3342870","url":null,"abstract":"Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on several key assumptions about the behavior of geotagged and non-geotagged users. However, these assumptions have not been fully validated. Lack of understanding the geotagging behavior prohibits people further utilizing it. In this paper, we present an empirical study of geotagging behavior on Twitter based on more than 40 billion tweets collected from 20 million users. There are three main findings that may challenge these common assumptions. Firstly, different groups of users have different geotagging preferences. For example, less than 3% of users speaking in Korean are geotagged, while more than 40% of users speaking in Indonesian use geotags. Secondly, users who report their locations in profiles are more likely to use geotags, which may affects the generability of those location prediction systems on non-geotagged users. Thirdly, strong homophily effect exists in users' geotagging behavior, that users tend to connect to friends with similar geotagging preferences.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129352745","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}
Today, with the emergence of various business review sites such as Yelp, Trip Advisor, and Zomato, people can write reviews and provide an assessment (often as 1–5 score rating). The success of a business on the crowd-sourced review platform has taken the form of positive reviews and high star ratings (failure are associated with negative reviews and low star ratings). We often claim that location plays a major role in determining the success or the failure of a given business. This paper attempts to verify this claim and quantifies the impact of location, solely, on business success, using two data sets; a Yelp dataset for business information and reviews, and another Location dataset that gathers location-based information in a city or an area. We perform an empirical study to quantify the impact of (i) relative location to well known landmarks and (ii) parameterized location (such as cost of living in a given zip code), on the success of restaurants. In our study, we found that parameterized location using location characteristic parameters such as housing affordability correlate highly with restaurant success with more than 0.81 correlation ratio. We also observe that the closer the restaurant to a landmark (relative location) the more likelihood it succeeds.
{"title":"Location, Location, Location! Quantifying the True Impact of Location on Business Reviews Using a Yelp Dataset","authors":"Abu Saleh Md Tayeen, Abderrahmen Mtibaa, S. Misra","doi":"10.1145/3341161.3345334","DOIUrl":"https://doi.org/10.1145/3341161.3345334","url":null,"abstract":"Today, with the emergence of various business review sites such as Yelp, Trip Advisor, and Zomato, people can write reviews and provide an assessment (often as 1–5 score rating). The success of a business on the crowd-sourced review platform has taken the form of positive reviews and high star ratings (failure are associated with negative reviews and low star ratings). We often claim that location plays a major role in determining the success or the failure of a given business. This paper attempts to verify this claim and quantifies the impact of location, solely, on business success, using two data sets; a Yelp dataset for business information and reviews, and another Location dataset that gathers location-based information in a city or an area. We perform an empirical study to quantify the impact of (i) relative location to well known landmarks and (ii) parameterized location (such as cost of living in a given zip code), on the success of restaurants. In our study, we found that parameterized location using location characteristic parameters such as housing affordability correlate highly with restaurant success with more than 0.81 correlation ratio. We also observe that the closer the restaurant to a landmark (relative location) the more likelihood it succeeds.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129818108","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}
In this work, we propose an optimal solution for face detection when operating in the thermal and visible bands. Our aim is to train, fine tune, optimize and validate preexisting object detection models using thermal and visible data separately. Thus, we perform an empirical study to determine the most efficient band specific DeepFace detection model in terms of detection performance. The original object detection models that were selected for our study are the Faster R-CNN (Region based Convolutional Neural Network), SSD (Single-shot Multi-Box Detector) and R-FCN (Region-based Fully Convolutional Network). Also, the dual-band dataset used for this work is composed of two challenging MWIR and visible band face datasets, where the faces were captured under variable conditions, i.e. indoors, outdoors, different standoff distances (5 and 10 meters) and poses. Experimental results show that the proposed detection model yields the highest accuracy independent of the band and scenario used. Specifically, we show that a modified and tuned Faster R-CNN architecture with ResNet 101 is the most promising model when compared to all the other models tested. The proposed model yields accuracy of 99.2% and 98.4% when tested on thermal and visible face data respectively. Finally, while the proposed model is relatively slower than its competitors, our further experiments show that the speed of this network can be increased by reducing the number of proposals in RPN (Region Proposal Network), and thus, the computational complexity challenge is significantly minimized.
{"title":"On Designing MWIR and Visible Band based DeepFace Detection Models","authors":"Suha Reddy Mokalla, T. Bourlai","doi":"10.1145/3341161.3343528","DOIUrl":"https://doi.org/10.1145/3341161.3343528","url":null,"abstract":"In this work, we propose an optimal solution for face detection when operating in the thermal and visible bands. Our aim is to train, fine tune, optimize and validate preexisting object detection models using thermal and visible data separately. Thus, we perform an empirical study to determine the most efficient band specific DeepFace detection model in terms of detection performance. The original object detection models that were selected for our study are the Faster R-CNN (Region based Convolutional Neural Network), SSD (Single-shot Multi-Box Detector) and R-FCN (Region-based Fully Convolutional Network). Also, the dual-band dataset used for this work is composed of two challenging MWIR and visible band face datasets, where the faces were captured under variable conditions, i.e. indoors, outdoors, different standoff distances (5 and 10 meters) and poses. Experimental results show that the proposed detection model yields the highest accuracy independent of the band and scenario used. Specifically, we show that a modified and tuned Faster R-CNN architecture with ResNet 101 is the most promising model when compared to all the other models tested. The proposed model yields accuracy of 99.2% and 98.4% when tested on thermal and visible face data respectively. Finally, while the proposed model is relatively slower than its competitors, our further experiments show that the speed of this network can be increased by reducing the number of proposals in RPN (Region Proposal Network), and thus, the computational complexity challenge is significantly minimized.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131119328","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}
Heterogeneous Information Networks (HINs) comprise nodes of different types inter-connected through diverse semantic relationships. In many real-world applications, nodes in information networks are often associated with additional attributes, resulting in Attributed HINs (or AHINs). In this paper, we study semi-supervised learning (SSL) on AHINs to classify nodes based on their structure, node types and attributes, given limited supervision. Recently, Graph Convolutional Networks (GCNs) have achieved impressive results in several graph-based SSL tasks. However, they operate on homogeneous networks, while being completely agnostic to the semantics of typed nodes and relationships in real-world HINs. In this paper, we seek to bridge the gap between semantic-rich HINs and the neighborhood aggregation paradigm of graph neural networks, to generalize GCNs through metagraph semantics. We propose a novel metagraph convolution operation to extract features from local metagraph-structured neighborhoods, thus capturing semantic higher-order relationships in AHINs. Our proposed neural architecture Meta-GNN extracts features of diverse semantics by utilizing multiple metagraphs, and employs a novel metagraph-attention module to learn personalized metagraph preferences for each node. Our semi-supervised node classification experiments on multiple real-world AHIN datasets indicate significant performance gains of 6% Micro-F1 on average over state-of-the-art AHIN baselines. Visualizations on metagraph attention weights yield interpretable insights into their relative task-specific importance.
{"title":"Meta-GNN: Metagraph Neural Network for Semi-supervised learning in Attributed Heterogeneous Information Networks","authors":"Aravind Sankar, Xinyang Zhang, K. Chang","doi":"10.1145/3341161.3342859","DOIUrl":"https://doi.org/10.1145/3341161.3342859","url":null,"abstract":"Heterogeneous Information Networks (HINs) comprise nodes of different types inter-connected through diverse semantic relationships. In many real-world applications, nodes in information networks are often associated with additional attributes, resulting in Attributed HINs (or AHINs). In this paper, we study semi-supervised learning (SSL) on AHINs to classify nodes based on their structure, node types and attributes, given limited supervision. Recently, Graph Convolutional Networks (GCNs) have achieved impressive results in several graph-based SSL tasks. However, they operate on homogeneous networks, while being completely agnostic to the semantics of typed nodes and relationships in real-world HINs. In this paper, we seek to bridge the gap between semantic-rich HINs and the neighborhood aggregation paradigm of graph neural networks, to generalize GCNs through metagraph semantics. We propose a novel metagraph convolution operation to extract features from local metagraph-structured neighborhoods, thus capturing semantic higher-order relationships in AHINs. Our proposed neural architecture Meta-GNN extracts features of diverse semantics by utilizing multiple metagraphs, and employs a novel metagraph-attention module to learn personalized metagraph preferences for each node. Our semi-supervised node classification experiments on multiple real-world AHIN datasets indicate significant performance gains of 6% Micro-F1 on average over state-of-the-art AHIN baselines. Visualizations on metagraph attention weights yield interpretable insights into their relative task-specific importance.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129234741","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}
Understanding the evolution of large-scale cooperation is important for the social welfare and stability of economic and social networks. Therefore, there is a need to model real-world scenarios that involve a trade-off between self-interest and social welfare with minimal artificial assumptions or constraints in a versatile framework. In this paper, we build an agent-based model to simulate the dynamics of a multi-agent, bilateral, resource-exchange network. We analyze how various strategies employed by communities can improve or hurt community payoffs as well as the overall social welfare of the network. We also analyze the role of common knowledge in inducing cooperation in the network. Our experimental evidence from simulations confirms that carefully-designed trading mechanisms can indeed encourage cooperation among communities with various motivations.
{"title":"Modeling the Dynamics of Resource Exchange Networks","authors":"Haripriya Chakraborty, Liang Zhao","doi":"10.1145/3341161.3342904","DOIUrl":"https://doi.org/10.1145/3341161.3342904","url":null,"abstract":"Understanding the evolution of large-scale cooperation is important for the social welfare and stability of economic and social networks. Therefore, there is a need to model real-world scenarios that involve a trade-off between self-interest and social welfare with minimal artificial assumptions or constraints in a versatile framework. In this paper, we build an agent-based model to simulate the dynamics of a multi-agent, bilateral, resource-exchange network. We analyze how various strategies employed by communities can improve or hurt community payoffs as well as the overall social welfare of the network. We also analyze the role of common knowledge in inducing cooperation in the network. Our experimental evidence from simulations confirms that carefully-designed trading mechanisms can indeed encourage cooperation among communities with various motivations.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126972322","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}
Role discovery and community detection in networks are two essential tasks in network analytics where the role denotes the global structural patterns of nodes in networks and the community represents the local connections of nodes in networks. Previous studies viewed these two tasks orthogonally and solved them independently while the relation between them has been totally neglected. However, it is intuitive that roles and communities in a network are correlated and complementary to each other. In this paper, we propose a novel model for simultaneous roles and communities detection (REACT) in networks. REACT uses non-negative matrix tri-factorization (NMTF) to detect roles and communities and utilizes L2,1 norm as the regularization to capture the diversity relation between roles and communities. The proposed model has several advantages comparing with other existing methods: (1) it incorporates the diversity relation between roles and communities to detect them simultaneously using a unified model, and (2) it provides extra information about the interaction patterns between roles and between communities using NMTF. To analyze the performance of REACT, we conduct experiments on several real-world SNs from different domains. By comparing with state-of-the-art community detection and role discovery methods, the obtained results demonstrate REACT performs best for both role and community detection tasks. Moreover, our model provides a better interpretation for the interaction patterns between communities and between roles.
{"title":"Joint Role and Community Detection in Networks via L2,1 Norm Regularized Nonnegative Matrix Tri-Factorization","authors":"Yulong Pei, G. Fletcher, Mykola Pechenizkiy","doi":"10.1145/3341161.3342886","DOIUrl":"https://doi.org/10.1145/3341161.3342886","url":null,"abstract":"Role discovery and community detection in networks are two essential tasks in network analytics where the role denotes the global structural patterns of nodes in networks and the community represents the local connections of nodes in networks. Previous studies viewed these two tasks orthogonally and solved them independently while the relation between them has been totally neglected. However, it is intuitive that roles and communities in a network are correlated and complementary to each other. In this paper, we propose a novel model for simultaneous roles and communities detection (REACT) in networks. REACT uses non-negative matrix tri-factorization (NMTF) to detect roles and communities and utilizes L2,1 norm as the regularization to capture the diversity relation between roles and communities. The proposed model has several advantages comparing with other existing methods: (1) it incorporates the diversity relation between roles and communities to detect them simultaneously using a unified model, and (2) it provides extra information about the interaction patterns between roles and between communities using NMTF. To analyze the performance of REACT, we conduct experiments on several real-world SNs from different domains. By comparing with state-of-the-art community detection and role discovery methods, the obtained results demonstrate REACT performs best for both role and community detection tasks. Moreover, our model provides a better interpretation for the interaction patterns between communities and between roles.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121766532","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}
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