Social Network Analysis

Research Area: Social Network Analysis

Research Area

We deal within the CAN group with the research field "Social Network Analysis".

In online social networks (OSNs) users voluntarily provide information about themselves, their interests, their friends and their activities, especially about their current situation or exceptional events. Nowadays these so called social signals are ubiquitous and can not only be collected from OSNs (e.g., friendships, interests, trust-relevant metadata), but also from applications (e.g., messaging or call patterns) and sensors (e.g., location). Social awareness harvests these signals, extracts useful and re-usable information (e.g., users’ social relationships, activity patterns, and interests), and exploits them in order to improve a service.

We develop new metrics, in order to identify important nodes in the social network. We investigate new communication paradigms emerging in group based messenger services. We optimize traffic management mechanisms by utilizing the information provided in social networks.

Researchers:

Anika Schwind M.Sc.

What's Up-Studie: Crowdfunding erfolgreich

Die Crowdfunding-Kampagne für unser Projekt "What's Up-Studie" ist vorbei und wir freuen uns, dass wir unser Funding-Ziel mit 13.668 € sogar überschreiten konnten. Vielen Dank an alle die das Projekt unterstützt haben! Jetzt können wir mit der Forschung beginnen um Depressionen bei Kindern und Jugendlichen mit Hilfe von WhatsApp zu bekämpfen.

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WhatsApp als Frühwarnsystem? Depression bei Jugendlichen erkennen

Wissenschaftler des Lehrstuhls für Informatik 3 und der Universität Tübingen prüfen, ob Veränderungen im Kommunikationsverhalten bei der Nutzung von WhatsApp zur Früherkennung von Depressionen genutzt werden können. Hierfür haben sie eine Crowdfunding-Kampagne gestartet: https://www.startnext.com/whatsapp-against-depression

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1500 chats analyzed with WhatsAnalyzer!

We are proud that in 2016 we have passed the great threshold of 1500 chats. Thus, we have gained chats and important data, which are used for scientific work. Thanks to all for contributing.

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Tool: WhatsAnalyzer

WhatsAnalyzer is a web-based tool to collect and analyze chat histories of the mobile messaging application WhatsApp. To protect the users’ privacy, the chats are anonymized during processing and only stored in that form. We implemented WhatsAnalyzer to investigate how group-based communication changes the activity patterns of multiple users. When evaluating communication technologies, models from the past (e.g., Poisson arrival process of end-to-end voice calls) cannot be directly used for nowadays applications (e.g., WhatsApp messages are exchanged in groups of users) and need to be adapted to integrate interaction of users on a smaller time scale. Therefore, we analyze the group communication behavior in WhatsApp and investigate possible implications of this emerging communication paradigm on networking technology.

The tool is open for everyone and can easily be used by every WhatsApp user. The user just has to select a chat from WhatsApp and send it to whatsanalyzer@uni-wuerzburg.de via email. As response, WhatsAnalyzer sends back an e-mail to the user including a link to his or her analysis visualized on a webpage.

For more information about WhatsAnalyzer, please visit the public webpage. If you have any further questions, feel free to send an e-mail to WhatsAnalyzer team.

Publications

2018[ to top ]

Potential Traffic Savings by Leveraging Proximity of Communication Groups in Mobile Messaging. Seufert, Michael; Schwind, Anika; Waigand, Marco; Hoßfeld, Tobias. In 14th International Conference on Network and Service Management (CNSM). Rome, Italy, 2018.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Communication groups in mobile messaging applications (MMAs) multiply the data transmissions, because every message has to be delivered to all members of the communication group. Thereby, they put a high load on mobile networks. As the number of recipients is still comparably small, the dataintensive user-generated content cannot be handled efficiently in large content delivery networks. However, small communication groups, such as groups of friends or teams, might often be in close proximity, which can be leveraged to locally deliver messages by applying edge caching or device-to-device (D2D) communication. In this work, a simulation study is conducted to investigate these potential traffic savings in the mobile network. It is based on a realistic communication model of the MMA WhatsApp and utilizes different models for human mobility. The user mobility and MMA communication are simulated for a single day in a small city to obtain the ratio of messages, which could be potentially transmitted locally when utilizing edge caching and D2D communication.

@inproceedings{info3-inproceedings-2018-40, abstract = {Communication groups in mobile messaging applications (MMAs) multiply the data transmissions, because every message has to be delivered to all members of the communication group. Thereby, they put a high load on mobile networks. As the number of recipients is still comparably small, the dataintensive user-generated content cannot be handled efficiently in large content delivery networks. However, small communication groups, such as groups of friends or teams, might often be in close proximity, which can be leveraged to locally deliver messages by applying edge caching or device-to-device (D2D) communication. In this work, a simulation study is conducted to investigate these potential traffic savings in the mobile network. It is based on a realistic communication model of the MMA WhatsApp and utilizes different models for human mobility. The user mobility and MMA communication are simulated for a single day in a small city to obtain the ratio of messages, which could be potentially transmitted locally when utilizing edge caching and D2D communication.}, address = {Rome, Italy}, author = {Seufert, Michael and Schwind, Anika and Waigand, Marco and Hoßfeld, Tobias}, booktitle = {14th International Conference on Network and Service Management (CNSM)}, keywords = {can}, month = 11, title = {Potential Traffic Savings by Leveraging Proximity of Communication Groups in Mobile Messaging}, year = 2018 }

%0 Conference Paper %1 info3-inproceedings-2018-40 %A Seufert, Michael %A Schwind, Anika %A Waigand, Marco %A Hoßfeld, Tobias %B 14th International Conference on Network and Service Management (CNSM) %C Rome, Italy %D 2018 %T Potential Traffic Savings by Leveraging Proximity of Communication Groups in Mobile Messaging %X Communication groups in mobile messaging applications (MMAs) multiply the data transmissions, because every message has to be delivered to all members of the communication group. Thereby, they put a high load on mobile networks. As the number of recipients is still comparably small, the dataintensive user-generated content cannot be handled efficiently in large content delivery networks. However, small communication groups, such as groups of friends or teams, might often be in close proximity, which can be leveraged to locally deliver messages by applying edge caching or device-to-device (D2D) communication. In this work, a simulation study is conducted to investigate these potential traffic savings in the mobile network. It is based on a realistic communication model of the MMA WhatsApp and utilizes different models for human mobility. The user mobility and MMA communication are simulated for a single day in a small city to obtain the ratio of messages, which could be potentially transmitted locally when utilizing edge caching and D2D communication.
WhatsAnalyzer: a Tool for Collecting and Analyzing WhatsApp Mobile Messaging Communication Data. Schwind, Anika; Seufert, Michael. 2018.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
WhatsAnalyzer is a web-based service, which collects and analyzes chat histories of the mobile messaging application WhatsApp. Thereby, it leverages the e-mail export feature of WhatsApp to obtain the chat histories, which cannot be accessed otherwise due to encrypted storage on the mobile device and end-to-end encrypted transmission over the Internet. Thus, the major asset of the service is that real communication data can be collected without the bias introduced by observing or surveying participants. The collected communication data can be analyzed and provides valuable insights into the communication in WhatsApp and the resulting network traffic. To incentivize users to send chat histories, the privacy of users is respected by anonymizing all communication data. Moreover, some analyses of each chat history can be accessed on a web page by the sender of the chats.

@misc{info3-demo-2018-2, abstract = {WhatsAnalyzer is a web-based service, which collects and analyzes chat histories of the mobile messaging application WhatsApp. Thereby, it leverages the e-mail export feature of WhatsApp to obtain the chat histories, which cannot be accessed otherwise due to encrypted storage on the mobile device and end-to-end encrypted transmission over the Internet. Thus, the major asset of the service is that real communication data can be collected without the bias introduced by observing or surveying participants. The collected communication data can be analyzed and provides valuable insights into the communication in WhatsApp and the resulting network traffic. To incentivize users to send chat histories, the privacy of users is respected by anonymizing all communication data. Moreover, some analyses of each chat history can be accessed on a web page by the sender of the chats.}, author = {Schwind, Anika and Seufert, Michael}, booktitle = {30th International Teletraffic Congress (ITC)}, keywords = {can}, title = {WhatsAnalyzer: a Tool for Collecting and Analyzing WhatsApp Mobile Messaging Communication Data}, year = 2018 }

%0 Generic %1 info3-demo-2018-2 %A Schwind, Anika %A Seufert, Michael %B 30th International Teletraffic Congress (ITC) %D 2018 %T WhatsAnalyzer: a Tool for Collecting and Analyzing WhatsApp Mobile Messaging Communication Data %X WhatsAnalyzer is a web-based service, which collects and analyzes chat histories of the mobile messaging application WhatsApp. Thereby, it leverages the e-mail export feature of WhatsApp to obtain the chat histories, which cannot be accessed otherwise due to encrypted storage on the mobile device and end-to-end encrypted transmission over the Internet. Thus, the major asset of the service is that real communication data can be collected without the bias introduced by observing or surveying participants. The collected communication data can be analyzed and provides valuable insights into the communication in WhatsApp and the resulting network traffic. To incentivize users to send chat histories, the privacy of users is respected by anonymizing all communication data. Moreover, some analyses of each chat history can be accessed on a web page by the sender of the chats.

2016[ to top ]

Group-based Communication in WhatsApp. Seufert, Michael; Hoßfeld, Tobias; Schwind, Anika; Burger, Valentin; Tran-Gia, Phuoc. In 1st IFIP Internet of People Workshop (IoP). Vienna, Austria, 2016.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
WhatsApp is a very popular mobile messaging application, which dominates today’s mobile communication. Especially the feature of group chats contributes to its success and changes the way people communicate. The group-based communication paradigm is investigated in this work, particularly focusing on the usage of WhatsApp, communication in group chats, and implications on mobile network traffic.

@inproceedings{info3-inproceedings-2016-1, abstract = {WhatsApp is a very popular mobile messaging application, which dominates today’s mobile communication. Especially the feature of group chats contributes to its success and changes the way people communicate. The group-based communication paradigm is investigated in this work, particularly focusing on the usage of WhatsApp, communication in group chats, and implications on mobile network traffic.}, address = {Vienna, Austria}, author = {Seufert, Michael and Hoßfeld, Tobias and Schwind, Anika and Burger, Valentin and Tran-Gia, Phuoc}, booktitle = {1st IFIP Internet of People Workshop (IoP)}, keywords = {can}, month = {05}, title = {Group-based Communication in WhatsApp}, year = 2016 }

%0 Conference Paper %1 info3-inproceedings-2016-1 %A Seufert, Michael %A Hoßfeld, Tobias %A Schwind, Anika %A Burger, Valentin %A Tran-Gia, Phuoc %B 1st IFIP Internet of People Workshop (IoP) %C Vienna, Austria %D 2016 %T Group-based Communication in WhatsApp %X WhatsApp is a very popular mobile messaging application, which dominates today’s mobile communication. Especially the feature of group chats contributes to its success and changes the way people communicate. The group-based communication paradigm is investigated in this work, particularly focusing on the usage of WhatsApp, communication in group chats, and implications on mobile network traffic.

2015[ to top ]

WiFi Offloading and Socially Aware Prefetching on Augmented Home Routers. Petropoulos, George; Lareida, Andri; Soursos, Sergios; Seufert, Michael; Burger, Valentin; Stiller, Burkhard. Clearwater Beach, FL, USA, 2015, October.
- [ BibTeX ]
- [ EndNote ]
@misc{info3-software-2015-25, address = {Clearwater Beach, FL, USA}, author = {Petropoulos, George and Lareida, Andri and Soursos, Sergios and Seufert, Michael and Burger, Valentin and Stiller, Burkhard}, booktitle = {40th Annual IEEE Conference on Local Computer Networks (LCN), Best Demo Award}, keywords = {sys:hidden:software}, month = 10, title = {WiFi Offloading and Socially Aware Prefetching on Augmented Home Routers}, year = 2015 }

%0 Generic %1 info3-software-2015-25 %A Petropoulos, George %A Lareida, Andri %A Soursos, Sergios %A Seufert, Michael %A Burger, Valentin %A Stiller, Burkhard %B 40th Annual IEEE Conference on Local Computer Networks (LCN), Best Demo Award %C Clearwater Beach, FL, USA %D 2015 %T WiFi Offloading and Socially Aware Prefetching on Augmented Home Routers
Analysis of Group-based Communication in WhatsApp. Seufert, Michael; Schwind, Anika; Hoßfeld, Tobias; Tran-Gia, Phuoc. In 7th EAI International Conference on Mobile Networks and Management (MONAMI). Santander, Spain, 2015.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
This work investigates group-based communication in WhatsApp based on a survey and the analysis of messaging logs. The characteristics of WhatsApp group chats in terms of usage and topics are outlined. We present a classification based on the topic of the group and classify anonymized messaging logs based on message statistics. Finally, we model WhatsApp group communication with a semi-Markov process, which can be used to generate network traffic similar to real messaging logs.

@inproceedings{info3-inproceedings-2015-523, abstract = {This work investigates group-based communication in WhatsApp based on a survey and the analysis of messaging logs. The characteristics of WhatsApp group chats in terms of usage and topics are outlined. We present a classification based on the topic of the group and classify anonymized messaging logs based on message statistics. Finally, we model WhatsApp group communication with a semi-Markov process, which can be used to generate network traffic similar to real messaging logs.}, address = {Santander, Spain}, author = {Seufert, Michael and Schwind, Anika and Hoßfeld, Tobias and Tran-Gia, Phuoc}, booktitle = {7th EAI International Conference on Mobile Networks and Management (MONAMI)}, keywords = {smartenit}, month = {09}, title = {Analysis of Group-based Communication in WhatsApp}, year = 2015 }

%0 Conference Paper %1 info3-inproceedings-2015-523 %A Seufert, Michael %A Schwind, Anika %A Hoßfeld, Tobias %A Tran-Gia, Phuoc %B 7th EAI International Conference on Mobile Networks and Management (MONAMI) %C Santander, Spain %D 2015 %T Analysis of Group-based Communication in WhatsApp %X This work investigates group-based communication in WhatsApp based on a survey and the analysis of messaging logs. The characteristics of WhatsApp group chats in terms of usage and topics are outlined. We present a classification based on the topic of the group and classify anonymized messaging logs based on message statistics. Finally, we model WhatsApp group communication with a semi-Markov process, which can be used to generate network traffic similar to real messaging logs.

2014[ to top ]

On the computation of entropy production in stationary social networks. Hoßfeld, Tobias; Burger, Valentin; Hinrichsen, Haye; Hirth, Matthias; Tran-Gia, Phuoc. In Social Network Analysis and Mining, 4. 2014.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Completing their initial phase of rapid growth, social networks are expected to reach a plateau from where on they are in a statistically stationary state. Such stationary conditions may have different dynamical properties. For example, if each message in a network is followed by a reply in opposite direction, the dynamics is locally balanced. Otherwise, if messages are ignored or forwarded to a different user, one may reach a stationary state with a directed flow of information. To distinguish between the two situations, we propose a quantity called entropy production that was introduced in statistical physics as a measure for non-vanishing probability currents in nonequilibrium stationary states. The proposed quantity closes a gap for characterizing online social networks. As major contribution, we show the relation and difference between entropy production and existing metrics. The comparison shows that computational intensive metrics like centrality can be approximated by entropy production for typical online social networks. To compute the entropy production from real-world measurements, the need for Bayesian inference and the limits of naïve estimates for those probability currents are shown. As further contribution, a general scheme is presented to measure the entropy production in small-world networks using Bayesian inference. The scheme is then applied for a specific example of the R mailing list.

@article{info3-article-2014-123, abstract = {Completing their initial phase of rapid growth, social networks are expected to reach a plateau from where on they are in a statistically stationary state. Such stationary conditions may have different dynamical properties. For example, if each message in a network is followed by a reply in opposite direction, the dynamics is locally balanced. Otherwise, if messages are ignored or forwarded to a different user, one may reach a stationary state with a directed flow of information. To distinguish between the two situations, we propose a quantity called entropy production that was introduced in statistical physics as a measure for non-vanishing probability currents in nonequilibrium stationary states. The proposed quantity closes a gap for characterizing online social networks. As major contribution, we show the relation and difference between entropy production and existing metrics. The comparison shows that computational intensive metrics like centrality can be approximated by entropy production for typical online social networks. To compute the entropy production from real-world measurements, the need for Bayesian inference and the limits of naïve estimates for those probability currents are shown. As further contribution, a general scheme is presented to measure the entropy production in small-world networks using Bayesian inference. The scheme is then applied for a specific example of the R mailing list.}, author = {Hoßfeld, Tobias and Burger, Valentin and Hinrichsen, Haye and Hirth, Matthias and Tran-Gia, Phuoc}, journal = {Social Network Analysis and Mining}, keywords = {sys:hidden:article}, month = {04}, title = {On the computation of entropy production in stationary social networks}, volume = 4, year = 2014 }

%0 Journal Article %1 info3-article-2014-123 %A Hoßfeld, Tobias %A Burger, Valentin %A Hinrichsen, Haye %A Hirth, Matthias %A Tran-Gia, Phuoc %D 2014 %J Social Network Analysis and Mining %T On the computation of entropy production in stationary social networks %V 4 %X Completing their initial phase of rapid growth, social networks are expected to reach a plateau from where on they are in a statistically stationary state. Such stationary conditions may have different dynamical properties. For example, if each message in a network is followed by a reply in opposite direction, the dynamics is locally balanced. Otherwise, if messages are ignored or forwarded to a different user, one may reach a stationary state with a directed flow of information. To distinguish between the two situations, we propose a quantity called entropy production that was introduced in statistical physics as a measure for non-vanishing probability currents in nonequilibrium stationary states. The proposed quantity closes a gap for characterizing online social networks. As major contribution, we show the relation and difference between entropy production and existing metrics. The comparison shows that computational intensive metrics like centrality can be approximated by entropy production for typical online social networks. To compute the entropy production from real-world measurements, the need for Bayesian inference and the limits of naïve estimates for those probability currents are shown. As further contribution, a general scheme is presented to measure the entropy production in small-world networks using Bayesian inference. The scheme is then applied for a specific example of the R mailing list.

Student Works and Theses

2018[ to top ]

WhatsAnalyzer: a Tool for Collecting and Analyzing WhatsApp Mobile Messaging Communication Data. Schwind, Anika; Seufert, Michael. 2018.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
WhatsAnalyzer is a web-based service, which collects and analyzes chat histories of the mobile messaging application WhatsApp. Thereby, it leverages the e-mail export feature of WhatsApp to obtain the chat histories, which cannot be accessed otherwise due to encrypted storage on the mobile device and end-to-end encrypted transmission over the Internet. Thus, the major asset of the service is that real communication data can be collected without the bias introduced by observing or surveying participants. The collected communication data can be analyzed and provides valuable insights into the communication in WhatsApp and the resulting network traffic. To incentivize users to send chat histories, the privacy of users is respected by anonymizing all communication data. Moreover, some analyses of each chat history can be accessed on a web page by the sender of the chats.

@misc{info3-demo-2018-2, abstract = {WhatsAnalyzer is a web-based service, which collects and analyzes chat histories of the mobile messaging application WhatsApp. Thereby, it leverages the e-mail export feature of WhatsApp to obtain the chat histories, which cannot be accessed otherwise due to encrypted storage on the mobile device and end-to-end encrypted transmission over the Internet. Thus, the major asset of the service is that real communication data can be collected without the bias introduced by observing or surveying participants. The collected communication data can be analyzed and provides valuable insights into the communication in WhatsApp and the resulting network traffic. To incentivize users to send chat histories, the privacy of users is respected by anonymizing all communication data. Moreover, some analyses of each chat history can be accessed on a web page by the sender of the chats.}, author = {Schwind, Anika and Seufert, Michael}, booktitle = {30th International Teletraffic Congress (ITC)}, keywords = {can}, title = {WhatsAnalyzer: a Tool for Collecting and Analyzing WhatsApp Mobile Messaging Communication Data}, year = 2018 }

%0 Generic %1 info3-demo-2018-2 %A Schwind, Anika %A Seufert, Michael %B 30th International Teletraffic Congress (ITC) %D 2018 %T WhatsAnalyzer: a Tool for Collecting and Analyzing WhatsApp Mobile Messaging Communication Data %X WhatsAnalyzer is a web-based service, which collects and analyzes chat histories of the mobile messaging application WhatsApp. Thereby, it leverages the e-mail export feature of WhatsApp to obtain the chat histories, which cannot be accessed otherwise due to encrypted storage on the mobile device and end-to-end encrypted transmission over the Internet. Thus, the major asset of the service is that real communication data can be collected without the bias introduced by observing or surveying participants. The collected communication data can be analyzed and provides valuable insights into the communication in WhatsApp and the resulting network traffic. To incentivize users to send chat histories, the privacy of users is respected by anonymizing all communication data. Moreover, some analyses of each chat history can be accessed on a web page by the sender of the chats.

Research Area: Social Network Analysis

Research Area

Researchers:

What's Up-Studie: Crowdfunding erfolgreich

WhatsApp als Frühwarnsystem? Depression bei Jugendlichen erkennen

1500 chats analyzed with WhatsAnalyzer!

Tool: WhatsAnalyzer

Publications

Student Works and Theses

Hinweis zum Datenschutz

Hinweis zum Datenschutz

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