Adding all prev year publications

Author: RSTZZZ

_posts/papers/2010-12-13-10.1109-ICDMW.2010.40.md

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+---
+title: 'Meerkat: Community Mining with Dynamic Social Networks'
+venue: 2010 IEEE International Conference on Data Mining Workshops
+names: Jiyang Chen, Justin Fagnan, R. Goebel, Reihaneh Rabbany, Farzad Sangi, M. Takaffoli,
+  Eric Verbeek, Osmar R Zaiane
+tags:
+- 2010 IEEE International Conference on Data Mining Workshops
+link: https://doi.org/10.1109/ICDMW.2010.40
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+Meerkat is a tool for visualization and community mining of social networks. It is being developed to offer novel algorithms and functionality that other tools do not possess. Meerkat’s features include navigation through graphical representations of networks, network querying and filtering, a multitude of graphical layout algorithms, community mining using recently developed algorithms, and dynamic network event analysis using recently published algorithms. These features will allow more insightful exploratory analysis and more robust inferences about communities and the significance of entity relationships. Meerkat is under active development, and future features will include additional options for community mining and visualization, focusing on algorithms and user interface designs not existing in other social network analysis tools.

_posts/papers/2011-01-01-4fb130483a0dc60129ec6cd8547eddc034db28a1.md

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+---
+title: Analyzing Participation of Students in Online Courses Using Social Network
+  Analysis Techniques
+venue: Educational Data Mining
+names: Reihaneh Rabbany, M. Takaffoli, Osmar R Zaiane
+tags:
+- Educational Data Mining
+link: https://www.semanticscholar.org/paper/4fb130483a0dc60129ec6cd8547eddc034db28a1
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+There is a growing number of courses delivered using e-learning environments and their online discussions play an important role in collaborative learning of students. Even in courses with a few number of students, there could be thousands of messages generated in a few months within these forums. Manually evaluating the participation of students in such case is a significant challenge, considering the fact that current e-learning environments do not provide much information regarding the structure of interactions between students.There is a recent line of research on applying social network analysis (SNA) techniques to study these interactions. And it is interesting to investigate the practicability of SNA in evaluating participation of students. Here we propose to exploit SNA techniques, including community mining, in order to discover relevant structures in social networks we generate from student communications but also information networks we produce from the content of the exchanged messages. With visualization of these discovered relevant structures and the automated identification of central and peripheral participants, an instructor is provided with better means to assess participation in the online discussions. We implemented these new ideas in a toolbox, named Meerkat-ED. Which prepares and visualizes overall snapshots of the participants in the discussion forums, their interactions, and the leader/peripheral students. Moreover, it creates a hierarchical summarization of the discussed topics, which gives the instructor a quick view of what is under discussion. We believe exploiting the mining abilities of this toolbox would facilitate fair evaluation of students’ participation in online courses.

_posts/papers/2011-11-18-10.1109-WSE.2011.6081829.md

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+---
+title: Web service matching for RESTful web services
+venue: Symposium on Web Systems Evolution
+names: Reihaneh Rabbany, Eleni Stroulia, Osmar R Zaiane
+tags:
+- Symposium on Web Systems Evolution
+link: https://doi.org/10.1109/WSE.2011.6081829
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+There is a growing number of web services available on the Internet, providing a wide range of functionalities. This diversity introduces a variety of new challenges in the field of software engineering — service discovery, integration, and composition, all of which require, to some extent, “service matching”. Web-service matching (or alignment) is the task of mapping the functionalities of two web services, assuming that these functionalities overlap somewhat. In this paper we propose a novel graph-theoretic approach, called Semantic Flow Matching (SFM), for matching REST web services, specified in WADL (Web Application Description Language). The method builds a heterogeneous network of WADL elements and semantically related terms, and uses this network to match similar functionalities of different web services. The method is implemented in a prototype tool that consists of two modules: a converter and a mapper; where the converter wraps the REST web services in WADL format and the mapper module matches web services based on their semantics extracted from the WADL interface build by the converter. We demonstrate the potential of the approach with a small case study.

_posts/papers/2011-12-11-10.1109-ICDMW.2011.12.md

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+---
+title: A Diffusion of Innovation-Based Closeness Measure for Network Associations
+venue: 2011 IEEE 11th International Conference on Data Mining Workshops
+names: Reihaneh Rabbany, Osmar R Zaiane
+tags:
+- 2011 IEEE 11th International Conference on Data Mining Workshops
+link: https://doi.org/10.1109/ICDMW.2011.12
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+Network association is a prevalent representation when dealing with data from present-day applications. Examples are crime event connections in criminology, cellphone call graphs in telecommunication, co-authorship networks in bibliometrics, etc. A large body of work has been devoted to the analysis of these networks and the discovery of their underlying structures. One important structure is the notion of community i.e. a group of nodes that are relatively cohesive within and reasonably disjointed outside. Finding the communities usually relies on a closeness/distance measure between network nodes. In this paper, we propose a novel closeness measure, named iCloseness, inspired by the theory of Diffusion of Innovations in anthropology. It is computed based on the intersection of neighbourhoods and quantifies the closeness of two nodes. To apply this measure we adjusted the Top Leaders community mining method to use this measure for community detection. Experimental results on real world and synthesized information networks show the effectiveness of our proposed measure and highly motivate the application of the iCloseness measure in the context of community mining.

_posts/papers/2012-05-01-10.1145-2207243.2207247.md

@@ -0,0 +1,23 @@
+---
+title: Social network analysis and mining to support the assessment of on-line student
+  participation
+venue: SKDD
+names: Reihaneh Rabbany, M. Takaffoli, Osmar R Zaiane
+tags:
+- SKDD
+link: https://doi.org/10.1145/2207243.2207247
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+There is a growing number of courses delivered using elearning environments and their online discussions play an important role in collaborative learning of students. Even in courses with a few number of students, there could be thousands of messages generated in a few months within these forums. Manually evaluating the participation of students in such case is a significant challenge, considering the fact that current e-learning environments do not provide much information regarding the structure of interactions between students. There is a recent line of research on applying social network analysis (SNA) techniques to study these interactions.
+ Here we propose to exploit SNA techniques, including community mining, in order to discover relevant structures in social networks we generate from student communications but also information networks we produce from the content of the exchanged messages. With visualization of these discovered relevant structures and the automated identification of central and peripheral participants, an instructor is provided with better means to assess participation in the online discussions. We implemented these new ideas in a toolbox, named Meerkat-ED, which automatically discovers relevant network structures, visualizes overall snapshots of interactions between the participants in the discussion forums, and outlines the leader/peripheral students. Moreover, it creates a hierarchical summarization of the discussed topics, which gives the instructor a quick view of what is under discussion. We believe exploiting the mining abilities of this toolbox would facilitate fair evaluation of students' participation in online courses.

_posts/papers/2012-08-26-10.1007-978-1-4614-6170-8_356.md

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+---
+title: Relative Validity Criteria for Community Mining Algorithms
+venue: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis
+  and Mining
+names: Reihaneh Rabbany, M. Takaffoli, Justin Fagnan, Osmar R Zaiane, R. Campello
+tags:
+- 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and
+  Mining
+link: https://doi.org/10.1007/978-1-4614-6170-8_356
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+None

_posts/papers/2013-01-01-fdd66d84ae6d870337e3cb8437d9b3efd688f546.md

@@ -0,0 +1,21 @@
+---
+title: Relative Validity Criteria for Community Mining Algorithms Synonyms Evaluation
+venue: ''
+names: Reihaneh Rabbany, Mansoreh Takaffoli, Justin Fagnan, Osmar R. Zäıane, R. Campello
+tags:
+- ''
+link: https://www.semanticscholar.org/paper/fdd66d84ae6d870337e3cb8437d9b3efd688f546
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+Grouping data points is one of the fundamental tasks in data mining, which is commonly known as clustering if data points are described by attributes. When dealing with interrelated data data represented in the form of nodes and their relationships and the connectivity is considered for grouping but not the node attributes, this task is also referred to as community mining. There has been a considerable number of approaches proposed in recent years for mining communities in a given network. However, little work has been done on how to evaluate community mining results. The common practice is to use an agreement measure to compare the mining result against a ground truth, however, the ground truth is not known in most of the real world applications. In this article, we investigate relative clustering quality measures defined for evaluation of clustering data points with attributes and propose proper adaptations to make them applicable in the context of social networks. Not only these relative criteria could be used as metrics for evaluating quality of the groupings but also they could be used as objectives for designing new community mining algorithms.

_posts/papers/2013-08-25-10.1145-2492517.2492633.md

@@ -0,0 +1,21 @@
+---
+title: Incremental local community identification in dynamic social networks
+venue: International Conference on Advances in Social Networks Analysis and Mining
+names: M. Takaffoli, Reihaneh Rabbany, Osmar R Zaiane
+tags:
+- International Conference on Advances in Social Networks Analysis and Mining
+link: https://doi.org/10.1145/2492517.2492633
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+Social networks are usually drawn from the interactions between individuals, and therefore are temporal and dynamic in essence. Examining how the structure of these networks changes over time provides insights into their evolution patterns, factors that trigger the changes, and ultimately predict the future structure of these networks. One of the key structural characteristics of networks is their community structure -groups of densely interconnected nodes. Communities in a dynamic social network span over periods of time and are affected by changes in the underlying population, i.e. they have fluctuating members and can grow and shrink over time. In this paper, we introduce a new incremental community mining approach, in which communities in the current time are obtained based on the communities from the past time frame. Compared to previous independent approaches, this incremental approach is more effective at detecting stable communities over time. Extensive experimental studies on real datasets, demonstrate the applicability, effectiveness, and soundness of our proposed framework.

_posts/papers/2013-10-29-10.1007-s13278-013-0132-x.md

@@ -0,0 +1,21 @@
+---
+title: 'Communities validity: methodical evaluation of community mining algorithms'
+venue: Social Network Analysis and Mining
+names: Reihaneh Rabbany, M. Takaffoli, Justin Fagnan, Osmar R Zaiane, R. Campello
+tags:
+- Social Network Analysis and Mining
+link: https://doi.org/10.1007/s13278-013-0132-x
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+None

_posts/papers/2014-01-01-10.1007-978-3-319-02738-8_16.md

@@ -0,0 +1,22 @@
+---
+title: 'Collaborative Learning of Students in Online Discussion Forums: A Social Network
+  Analysis Perspective'
+venue: ''
+names: Reihaneh Rabbany, Samira ElAtia, M. Takaffoli, Osmar R Zaiane
+tags:
+- ''
+link: https://doi.org/10.1007/978-3-319-02738-8_16
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+None