Pub Date : 2023-10-12DOI: 10.1007/s13278-023-01137-1
Maria Predari, Lukas Berner, Robert Kooij, Henning Meyerhenke
Abstract The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G . We consider two optimization problems of adding k new edges to G such that the resulting graph has minimal total effective resistance (i.e., is most robust)—one where the new edges can be anywhere in the graph and one where the new edges need to be incident to a specified focus node. The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion, yet this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in an established generic greedy heuristic. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph- and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process larger graphs for which the application of the state-of-the-art greedy approach was impractical before.
{"title":"Greedy optimization of resistance-based graph robustness with global and local edge insertions","authors":"Maria Predari, Lukas Berner, Robert Kooij, Henning Meyerhenke","doi":"10.1007/s13278-023-01137-1","DOIUrl":"https://doi.org/10.1007/s13278-023-01137-1","url":null,"abstract":"Abstract The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G . We consider two optimization problems of adding k new edges to G such that the resulting graph has minimal total effective resistance (i.e., is most robust)—one where the new edges can be anywhere in the graph and one where the new edges need to be incident to a specified focus node. The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion, yet this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in an established generic greedy heuristic. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph- and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process larger graphs for which the application of the state-of-the-art greedy approach was impractical before.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"55 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968987","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 : 2023-10-06DOI: 10.1007/s13278-023-01136-2
Riccardo Dondi, Pietro Hiram Guzzi, Mohammad Mehdi Hosseinzadeh, Marianna Milano
Abstract Interactions among entities are usually modeled using graphs. In many real scenarios, these relations may change over time, and different kinds exist among entities that need to be integrated. We introduce a new network model called temporal dual network, to deal with interactions which change over time and to integrate information coming from two different networks. In this new model, we consider a fundamental problem in graph mining, that is, finding the densest subgraphs. To deal with this problem, we propose an approach that, given two temporal graphs, (1) produces a dual temporal graph via alignment and (2) asks for identifying the densest subgraphs in this resulting graph. For this latter problem, we present a polynomial-time dynamic programming algorithm and a faster heuristic based on constraining the dynamic programming to consider only bounded temporal graphs and a local search procedure. We show that our method can output solutions not far from the optimal ones, even for temporal graphs having 10000 vertices and 10000 timestamps. Finally, we present a case study on a real dual temporal network.
{"title":"Dense subgraphs in temporal social networks","authors":"Riccardo Dondi, Pietro Hiram Guzzi, Mohammad Mehdi Hosseinzadeh, Marianna Milano","doi":"10.1007/s13278-023-01136-2","DOIUrl":"https://doi.org/10.1007/s13278-023-01136-2","url":null,"abstract":"Abstract Interactions among entities are usually modeled using graphs. In many real scenarios, these relations may change over time, and different kinds exist among entities that need to be integrated. We introduce a new network model called temporal dual network, to deal with interactions which change over time and to integrate information coming from two different networks. In this new model, we consider a fundamental problem in graph mining, that is, finding the densest subgraphs. To deal with this problem, we propose an approach that, given two temporal graphs, (1) produces a dual temporal graph via alignment and (2) asks for identifying the densest subgraphs in this resulting graph. For this latter problem, we present a polynomial-time dynamic programming algorithm and a faster heuristic based on constraining the dynamic programming to consider only bounded temporal graphs and a local search procedure. We show that our method can output solutions not far from the optimal ones, even for temporal graphs having 10000 vertices and 10000 timestamps. Finally, we present a case study on a real dual temporal network.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350867","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 : 2023-10-04DOI: 10.1007/s13278-023-01138-0
Yuxin Zhang, Dafeng Xu
{"title":"Who joins which network, and why?","authors":"Yuxin Zhang, Dafeng Xu","doi":"10.1007/s13278-023-01138-0","DOIUrl":"https://doi.org/10.1007/s13278-023-01138-0","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135592175","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 : 2023-09-29DOI: 10.1007/s13278-023-01128-2
Dalia A. Rodríguez, Julia Diaz-Escobar, Arnoldo Díaz-Ramírez, Leonardo Trujillo
{"title":"Domain-adaptive pre-training on a BERT model for the automatic detection of misogynistic tweets in Spanish","authors":"Dalia A. Rodríguez, Julia Diaz-Escobar, Arnoldo Díaz-Ramírez, Leonardo Trujillo","doi":"10.1007/s13278-023-01128-2","DOIUrl":"https://doi.org/10.1007/s13278-023-01128-2","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135195130","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 : 2023-09-27DOI: 10.1007/s13278-023-01134-4
Mohammad Yousuf Mehmood, Syed Junaid Haqqani, Faraz Zaidi, Céline Rozenblat
{"title":"Economic hubs and the domination of inter-regional ties in world city networks","authors":"Mohammad Yousuf Mehmood, Syed Junaid Haqqani, Faraz Zaidi, Céline Rozenblat","doi":"10.1007/s13278-023-01134-4","DOIUrl":"https://doi.org/10.1007/s13278-023-01134-4","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135536743","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 : 2023-09-25DOI: 10.1007/s13278-023-01119-3
Mohammed Kasri, Anas El-Ansari, Mohamed El Fissaoui, Badreddine Cherkaoui, Marouane Birjali, Abderrahim Beni-Hssane
{"title":"Public sentiment toward renewable energy in Morocco: opinion mining using a rule-based approach","authors":"Mohammed Kasri, Anas El-Ansari, Mohamed El Fissaoui, Badreddine Cherkaoui, Marouane Birjali, Abderrahim Beni-Hssane","doi":"10.1007/s13278-023-01119-3","DOIUrl":"https://doi.org/10.1007/s13278-023-01119-3","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816860","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 : 2023-09-25DOI: 10.1007/s13278-023-01133-5
Ilana Porter, Bar Galam, Roni Ramon-Gonen
{"title":"Emotion detection and its influence on popularity in a social network-based on the American TV series friends","authors":"Ilana Porter, Bar Galam, Roni Ramon-Gonen","doi":"10.1007/s13278-023-01133-5","DOIUrl":"https://doi.org/10.1007/s13278-023-01133-5","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816861","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 : 2023-09-25DOI: 10.1007/s13278-023-01122-8
Saif Aldeen Madi, Giuseppe Pirrò
Abstract Community deception is about protecting users of a community from being discovered by community detection algorithms. This paper studies community deception in directed influence network (DIN). It aims to address the limitations of the state of the art through a twofold strategy: introducing directed influence and considering the role of nodes in the deception strategy. The study focuses on using modularity as the optimization function. It offers several contributions, including an upgraded version of modularity that accommodates the concept of influence, edge-based, and node-based deception algorithms.. The study concludes with a comparison of the proposed methods with the state of the art showing that not only influence is a valuable ingredient to devising deception strategies but also that novel deception approaches centered on node operations can be successfully devised.
{"title":"Community deception in directed influence networks","authors":"Saif Aldeen Madi, Giuseppe Pirrò","doi":"10.1007/s13278-023-01122-8","DOIUrl":"https://doi.org/10.1007/s13278-023-01122-8","url":null,"abstract":"Abstract Community deception is about protecting users of a community from being discovered by community detection algorithms. This paper studies community deception in directed influence network (DIN). It aims to address the limitations of the state of the art through a twofold strategy: introducing directed influence and considering the role of nodes in the deception strategy. The study focuses on using modularity as the optimization function. It offers several contributions, including an upgraded version of modularity that accommodates the concept of influence, edge-based, and node-based deception algorithms.. The study concludes with a comparison of the proposed methods with the state of the art showing that not only influence is a valuable ingredient to devising deception strategies but also that novel deception approaches centered on node operations can be successfully devised.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816848","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 : 2023-09-25DOI: 10.1007/s13278-023-01131-7
Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves
Abstract Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.
{"title":"Text mining of veterinary forums for epidemiological surveillance supplementation","authors":"Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves","doi":"10.1007/s13278-023-01131-7","DOIUrl":"https://doi.org/10.1007/s13278-023-01131-7","url":null,"abstract":"Abstract Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816853","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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