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Johnson, Henric
Publikasjoner (10 av 43) Visa alla publikasjoner
Liu, F., Zhu, X., Hu, Y., Ren, L. & Johnson, H. (2017). A cloud theory-based trust computing model in social networks. Entropy, 19(1), Article ID 11.
Åpne denne publikasjonen i ny fane eller vindu >>A cloud theory-based trust computing model in social networks
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2017 (engelsk)Inngår i: Entropy, ISSN 1099-4300, Vol. 19, nr 1, artikkel-id 11Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

How to develop a trust management model and then to efficiently control and manage nodes is an important issue in the scope of social network security. In this paper, a trust management model based on a cloud model is proposed. The cloud model uses a specific computation operator to achieve the transformation from qualitative concepts to quantitative computation. Additionally, this can also be used to effectively express the fuzziness, randomness and the relationship between them of the subjective trust. The node trust is divided into reputation trust and transaction trust. In addition, evaluation methods are designed, respectively. Firstly, the two-dimension trust cloud evaluation model is designed based on node's comprehensive and trading experience to determine the reputation trust. The expected value reflects the average trust status of nodes. Then, entropy and hyper-entropy are used to describe the uncertainty of trust. Secondly, the calculation methods of the proposed direct transaction trust and the recommendation transaction trust involve comprehensively computation of the transaction trust of each node. Then, the choosing strategies were designed for node to trade based on trust cloud. Finally, the results of a simulation experiment in P2P network file sharing on an experimental platform directly reflect the objectivity, accuracy and robustness of the proposed model, and could also effectively identify the malicious or unreliable service nodes in the system. In addition, this can be used to promote the service reliability of the nodes with high credibility, by which the stability of the whole network is improved. © 2016 by the authors.

Emneord
Cloud model, Reputation trust, Social network, Transaction trust, Trust evaluation
HSV kategori
Identifikatorer
urn:nbn:se:bth-13830 (URN)10.3390/e19010011 (DOI)000392978500011 ()2-s2.0-85009241681 (Scopus ID)
Merknad

Open access

Tilgjengelig fra: 2017-01-25 Laget: 2017-01-25 Sist oppdatert: 2018-01-13bibliografisk kontrollert
Erlandsson, F., Bródka, P., Boldt, M. & Johnson, H. (2017). Do We Really Need To Catch Them All?: A New User-Guided Social Media Crawling Method. Entropy, 19(12), Article ID 686.
Åpne denne publikasjonen i ny fane eller vindu >>Do We Really Need To Catch Them All?: A New User-Guided Social Media Crawling Method
2017 (engelsk)Inngår i: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 19, nr 12, artikkel-id 686Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

With the growing use of popular social media services like Facebook and Twitter it is hard to collect all content from the networks without access to the core infrastructure or paying for it. Thus, if all content cannot be collected one must consider which data are of most importance.In this work we present a novel User-Guided Social Media Crawling method (USMC) that is able to collect data from social media, utilizing the wisdom of the crowd to decide the order in which user generated content should be collected, to cover as many user interactions as possible. USMC is validated by crawling 160 Facebook public pages, containing 368 million users and 1.3 billion interactions, and it is compared with two other crawling methods. The results show that it is possible to cover approximately 75% of the interactions on a Facebook page by sampling just 20% of its posts, and at the same time reduce the crawling time by 53%.What is more, the social network constructed from the 20% sample has more than 75% of the users and edges compared to the social network created from all posts, and has very similar degree distribution.

sted, utgiver, år, opplag, sider
MDPI AG, 2017
Emneord
social media, social networks, sampling, crawling
HSV kategori
Identifikatorer
urn:nbn:se:bth-15508 (URN)10.3390/e19120686 (DOI)000419007900055 ()
Tilgjengelig fra: 2017-11-15 Laget: 2017-11-15 Sist oppdatert: 2018-01-26bibliografisk kontrollert
Osekowska, E., Johnson, H. & Carlsson, B. (2017). Maritime vessel traffic modeling in the context of concept drift. In: Transportation Research Procedia: . Paper presented at World Conference on Transport Research - WCTR 2016, Shanghai (pp. 1457-1476). Elsevier, 25
Åpne denne publikasjonen i ny fane eller vindu >>Maritime vessel traffic modeling in the context of concept drift
2017 (engelsk)Inngår i: Transportation Research Procedia, Elsevier, 2017, Vol. 25, s. 1457-1476Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Maritime traffic modeling serves the purpose of extracting human-readable information and discovering knowledge in the otherwise illegible mass of traffic data. The goal of this study is to examine the presence and character of fluctuations in maritime traffic patterns. The main objective is to identify such fluctuations and capture them in terms of a concept drift, i.e., unforeseen shifts in statistical properties of the modeled target occurring over time. The empirical study is based on a collection of AIS vessel tracking data, spanning over a year. The scope of the study limits the AIS data area to the Baltic region (9-31°E, 53-66°N), which experiences some of the most dense maritime traffic in the world. The investigations employ a novel maritime traffic modeling method based on the potential fields concept, adapted for this study to facilitate the examination of concept drift. The concept drift is made apparent in course of the statistical and visual analysis of the experimental results. This study shows a number of particular cases, in which the maritime traffic is affected by concept drifts of varying extent and character. The visual representations of the traffic models make shifts in the traffic patterns apparent and comprehensible to human eye. Based on the experimental outcomes, the robustness of the modeling method against concept drift in traffic is discussed and improvements are proposed. The outcomes provide insights into regularly reoccurring drifts and irregularities within the traffic data itself that may serve to further optimize the modeling method, and - in turn - the performance of detection based on it. © 2017 The Authors. Published by Elsevier B. V.

sted, utgiver, år, opplag, sider
Elsevier, 2017
Serie
Transportation Research Procedia
Emneord
anomaly detection, concept drift, maritime traffic, traffic modeling
HSV kategori
Identifikatorer
urn:nbn:se:bth-14677 (URN)10.1016/j.trpro.2017.05.173 (DOI)
Konferanse
World Conference on Transport Research - WCTR 2016, Shanghai
Tilgjengelig fra: 2017-06-22 Laget: 2017-06-22 Sist oppdatert: 2018-01-13bibliografisk kontrollert
Erlandsson, F., Bródka, P., Borg, A. & Johnson, H. (2016). Finding Influential Users in Social Media Using Association Rule Learning. Entropy, 18(5)
Åpne denne publikasjonen i ny fane eller vindu >>Finding Influential Users in Social Media Using Association Rule Learning
2016 (engelsk)Inngår i: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 18, nr 5Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

sted, utgiver, år, opplag, sider
Basel, Switzerland: MDPI AG, 2016
Emneord
social media, data mining, association rule learning, prediction, social network analysis
HSV kategori
Identifikatorer
urn:nbn:se:bth-13575 (URN)10.3390/e18050164 (DOI)000377262900009 ()
Merknad

open access

Tilgjengelig fra: 2016-12-12 Laget: 2016-12-12 Sist oppdatert: 2018-01-13bibliografisk kontrollert
Erlandsson, F., Borg, A., Johnson, H. & Bródka, P. (2016). Predicting User Participation in Social Media. In: Wierzbicki A., Brandes U., Schweitzer F., Pedreschi D. (Ed.), Lecture Notes in Computer Science: . Paper presented at 12th International Conference and School on Advances in Network Science, NetSci-X, Wroclaw, Poland (pp. 126-135). Springer, 9564
Åpne denne publikasjonen i ny fane eller vindu >>Predicting User Participation in Social Media
2016 (engelsk)Inngår i: Lecture Notes in Computer Science / [ed] Wierzbicki A., Brandes U., Schweitzer F., Pedreschi D., Springer, 2016, Vol. 9564, s. 126-135Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

Abstract Online social networking services like Facebook provides a popular way for users to participate in different communication groups and discuss relevant topics with each other. While users tend to have an impact on each other, it is important to better understand and ...

sted, utgiver, år, opplag, sider
Springer, 2016
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9564
HSV kategori
Identifikatorer
urn:nbn:se:bth-11533 (URN)10.1007/978-3-319-28361-6_10 (DOI)978-3-319-28360-9 (ISBN)
Konferanse
12th International Conference and School on Advances in Network Science, NetSci-X, Wroclaw, Poland
Tilgjengelig fra: 2016-02-02 Laget: 2016-02-02 Sist oppdatert: 2018-01-10bibliografisk kontrollert
Liu, F., Wang, L., Johnson, H. & Zhao, H. (2015). Analysis of network trust dynamics based on the evolutionary game. SCIENTIA IRANICA, 22(6), 2548-2557
Åpne denne publikasjonen i ny fane eller vindu >>Analysis of network trust dynamics based on the evolutionary game
2015 (engelsk)Inngår i: SCIENTIA IRANICA, ISSN 1026-3098, Vol. 22, nr 6, s. 2548-2557Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Trust, as a multi-disciplinary research domain, is of high importance in the area of network security and it has increasingly become an important mechanism to solve the issues of distributed network security. Trust is also an effective mechanism to simplify complex society, and is the source to promote personal or social cooperation. From the perspective of network ecological evolution, we propose the model of the P2P Social Ecological Network. Based on game theory, we also put forward network trust dynamics and network eco-evolution by analysis of network trust and the development of the dynamics model. In this article, we further analyze the dynamic equation, and the evolutionary trend of the trust relationship between nodes using the replicator dynamics principle. Finally, we reveal the law of trust evolution dynamics, and the simulation results clearly describe that the dynamics of trust can be effective in promoting the stability and evolution of networks. (C) 2015 Sharif University of Technology. All rights reserved.

sted, utgiver, år, opplag, sider
Elsevier, 2015
Emneord
Trust, Trust dynamics, Game theory, Evolutionary game
HSV kategori
Identifikatorer
urn:nbn:se:bth-11669 (URN)000369583600002 ()
Tilgjengelig fra: 2016-03-02 Laget: 2016-02-29 Sist oppdatert: 2018-01-10bibliografisk kontrollert
Erlandsson, F., Nia, R., Boldt, M., Johnson, H. & Wu, S. F. (2015). Crawling Online Social Networks. In: SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015): . Paper presented at Second European Network Intelligence Conference (ENIC) (pp. 9-16). IEEE Computer Society
Åpne denne publikasjonen i ny fane eller vindu >>Crawling Online Social Networks
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2015 (engelsk)Inngår i: SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015), IEEE Computer Society, 2015, s. 9-16Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Researchers put in tremendous amount of time and effort in order to crawl the information from online social networks. With the variety and the vast amount of information shared on online social networks today, different crawlers have been designed to capture several types of information. We have developed a novel crawler called SINCE. This crawler differs significantly from other existing crawlers in terms of efficiency and crawling depth. We are getting all interactions related to every single post. In addition, are we able to understand interaction dynamics, enabling support for making informed decisions on what content to re-crawl in order to get the most recent snapshot of interactions. Finally we evaluate our crawler against other existing crawlers in terms of completeness and efficiency. Over the last years we have crawled public communities on Facebook, resulting in over 500 million unique Facebook users, 50 million posts, 500 million comments and over 6 billion likes.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2015
Emneord
Crawlers;Facebook;Feeds;Informatics;Sampling methods;Silicon compounds;crawling;mining;online social media;online social networks
HSV kategori
Identifikatorer
urn:nbn:se:bth-10993 (URN)10.1109/ENIC.2015.10 (DOI)000375081700002 ()
Konferanse
Second European Network Intelligence Conference (ENIC)
Tilgjengelig fra: 2016-02-02 Laget: 2015-11-20 Sist oppdatert: 2018-01-10bibliografisk kontrollert
Osekowska, E., Johnson, H. & Carlsson, B. (2014). Grid size optimization for potential field based maritime anomaly detection. In: Benitez, FG Rossi, R (Ed.), 17TH MEETING OF THE EURO WORKING GROUP ON TRANSPORTATION, EWGT2014: . Paper presented at 17th Meeting of the EURO-Working-Group on Transportation (EWGT), JUL 02-04, 2014, Sevilla, SPAIN (pp. 720-729). ELSEVIER SCIENCE BV
Åpne denne publikasjonen i ny fane eller vindu >>Grid size optimization for potential field based maritime anomaly detection
2014 (engelsk)Inngår i: 17TH MEETING OF THE EURO WORKING GROUP ON TRANSPORTATION, EWGT2014 / [ed] Benitez, FG Rossi, R, ELSEVIER SCIENCE BV , 2014, s. 720-729Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This study focuses on improving the potential field based maritime data modeling method, developed to extract traffic patterns and detect anomalies, in a clear, understandable and informative way. The method's novelty lies in employing the concept of a potential field for AIS vessel tracking data abstraction and maritime traffic representation. Unlike the traditional maritime surveillance equipment, such as radar or GPS, the AIS system comprehensively represents the identity and properties of a vessel, as well as its behavior, thus preserving the effects of navigational decisions, based on the skills of experienced seamen. In the developed data modeling process, every vessel generates potential charges, which value represent the vessel's behavior, and drops the charges at locations it passes. Each AIS report is used to assign a potential charge at the reported vessel positions. The method derives three construction elements, which define, firstly, how charges are accumulated, secondly, how a charge decays over time, and thirdly, in what way the potential is distributed around the source charge. The collection of potential fields represents a model of normal behavior, and vessels not conforming to it are marked as anomalous. In the anomaly detection prototype system STRAND, the sensitivity of anomaly detection can be modified by setting a geographical coordinate grid precision to more dense or coarse. The objective of this study is to identify the optimal grid size for two different conditions an open sea and a port area case. A noticeable shift can be observed between the results for the open sea and the port area. The plotted detection rates converge towards an optimal ratio for smaller grid sizes in the port area (60-200 meters), than in the open sea case (300-1000 meters). The effective outcome of the potential filed based anomaly detection is filtering out all vessels behaving normally and presenting a set of anomalies, for a subsequent incident analysis using STRAND as an information visualization tool.

sted, utgiver, år, opplag, sider
ELSEVIER SCIENCE BV, 2014
Serie
Transportation Research Procedia, ISSN 2352-1465 ; 3
Emneord
anomaly detection, maritime traffic, potential field, AIS
HSV kategori
Identifikatorer
urn:nbn:se:bth-14579 (URN)10.1016/j.trpro.2014.10.051 (DOI)000377412600077 ()
Konferanse
17th Meeting of the EURO-Working-Group on Transportation (EWGT), JUL 02-04, 2014, Sevilla, SPAIN
Tilgjengelig fra: 2017-06-19 Laget: 2017-06-19 Sist oppdatert: 2017-06-19bibliografisk kontrollert
Brodka, P., Sobas, M. & Johnson, H. (2014). Profile Cloning Detection in Social Networks. In: 2014 EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC): . Paper presented at 1st European Network Intelligence Conference (ENIC), Wroclaw, POLAND (pp. 63-68). IEEE Computer Society
Åpne denne publikasjonen i ny fane eller vindu >>Profile Cloning Detection in Social Networks
2014 (engelsk)Inngår i: 2014 EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC), IEEE Computer Society, 2014, s. 63-68Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Profile cloning is a severe security issue in social networks since it is used to make a profile identical to existing ones. Profile cloning detection creates a possibility to detect frauds that would use people's trust to gather social information. This paper proposes two novel methods of profile cloning detection and also presents state-of-the-art research. The first method is based on the similarity of attributes from both profiles and the second method is based on the similarity of relationship networks. The methods are further evaluated with experiments and the results clearly describes that the proposed methods are useful and efficient compared to existing methods. The paper also stress that profile cloning in Facebook is not only possible but also fairly easy to perform.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2014
Emneord
anomaly detection, profile cloning, social network, facebook
HSV kategori
Identifikatorer
urn:nbn:se:bth-10855 (URN)10.1109/ENIC.2014.21 (DOI)000361480100010 ()978-1-4799-6914-2 (ISBN)
Konferanse
1st European Network Intelligence Conference (ENIC), Wroclaw, POLAND
Tilgjengelig fra: 2015-10-20 Laget: 2015-10-20 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Nia, R., Erlandsson, F., Johnson, H. & Wu, S. F. (2013). Leveraging Social Interactions to Suggest Friends. In: IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW): . Paper presented at 33rd International Conference on Distributed Computing Systems Workshops (ICDCS 2013 Workshops), Philadelphia, PA, USA (pp. 386-391). IEEE Computer Society
Åpne denne publikasjonen i ny fane eller vindu >>Leveraging Social Interactions to Suggest Friends
2013 (engelsk)Inngår i: IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW), IEEE Computer Society, 2013, s. 386-391Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Over the past decade Online Social Networks (OSNs) have made it possible for people to stay in touch with people they already know in real life; although, they have not been able to allow users to grow their personal social network. Existence of many successful dating and friend finder applications online today show the need and importance of such applications. In this paper, we describe an application that leverages social interactions in order to suggest people to users that they may find interesting. We allow users to expand their personal social network using their own interactions with other users on public pages and groups in OSNs. We finally evaluate our application by selecting a random set of users and asking them for their honest opinion.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2013
Emneord
social networking (online), OSN, dating applications, friend finder applications, friend suggestion, online social networks, personal social network, social interactions, Communities, Context, Electronic mail, Facebook, Privacy, Silicon compounds, Social Networks
HSV kategori
Identifikatorer
urn:nbn:se:bth-10891 (URN)10.1109/ICDCSW.2013.93 (DOI)000332852800067 ()978-1-4799-3247-4 (ISBN)
Konferanse
33rd International Conference on Distributed Computing Systems Workshops (ICDCS 2013 Workshops), Philadelphia, PA, USA
Tilgjengelig fra: 2015-10-27 Laget: 2015-10-27 Sist oppdatert: 2018-01-17bibliografisk kontrollert
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