Journal of Homeland Security and Emergency Management
Emergent volunteer groups play a significant role during disasters. There is a rich literature on... more Emergent volunteer groups play a significant role during disasters. There is a rich literature on the role of volunteer groups in disasters and disaster volunteerism. However, the rapid proliferation of social media platforms in the last decade made a significant impact on human lives, and disaster volunteerism is no exception. This article argues that there is a need for understanding social media’s impact on disaster volunteerism. Using Harvey as a case, this article analyzes 74 Facebook groups that were created during the storm. The article compares the emergence and lifespan, structure, and function of online volunteer groups to those of volunteer groups before social media. Findings show important distinctions between online groups and those mentioned in the literature. First, online groups are easier to observe and analyze because of the digital traces they leave. Online groups emerge in different phases of disaster (response, early recovery) depending on people’s needs. Their...
2021 IEEE International Conference on Big Data (Big Data)
A drug-drug interaction (DDI) occurs when a drug is combined with other drug(s). DDIs have the po... more A drug-drug interaction (DDI) occurs when a drug is combined with other drug(s). DDIs have the potential to obstruct, increase, or diminish the intended impact of a drug or, in the worst-case scenario, induce an undesirable side effect. While it is critical to discover DDIs during clinical trials, it is impractical and expensive to detect all possible DDIs for a drug. Although several computational approaches for this problem have been developed, many of these methods need external biomedical knowledge that makes them difficult to generalize to drugs in early development phase. In this paper, we propose a novel method for predicting DDIs based on the vital chemical substructure of drugs extracted from their SMILES strings. We construct a graph that connects drugs based on their common functional chemical substructures. Furthermore, we apply different well-known graph neural network (GNN) methods to generate drug embeddings. Drug embeddings of individual drugs are concatenated to generate features of drug pairs. Finally, drug pair features are fed to different machine learning (ML) classifiers for DDI prediction. We evaluate our model on DrugBank dataset. Our result shows promising results and our model outperforms a baseline model based on different DDI representation creation methods.
ASPECT BASED OPINION MINING ON TURKISH TWEETS Esra AkbaÅŸ M.S. in Computer Engineering Supervisor:... more ASPECT BASED OPINION MINING ON TURKISH TWEETS Esra AkbaÅŸ M.S. in Computer Engineering Supervisor: Assoc. Prof. Dr. Hakan FerhatosmanoÄŸlu July, 2012 Understanding opinions about entities or brands is instrumental in reputation management and decision making. With the advent of social media, more people are willing to publicly share their recommendations and opinions. As the type and amount of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. Sentiment classification aims to identify the polarity of sentiment in text. The polarity is predicted on either a binary (positive, negative) or a multi-variant scale as the strength of sentiment expressed. Text often contains a mix of positive and negative sentiments, hence it is often necessary to detect both simultaneously. While classifying text based on sentiment polarity is a major task, analyzing sentiments separately for each aspect can be more useful in many applications...
We consider the community search problem defined upon a large graph G : given a query vertex q in... more We consider the community search problem defined upon a large graph G : given a query vertex q in G , to find as output all the densely connected subgraphs of G , each of which contains the query v . As an online, query-dependent variant of the well-known community detection problem, community search enables personalized community discovery that has found widely varying applications in real-world, large-scale graphs. In this paper, we study the community search problem in the truss-based model aimed at discovering all dense and cohesive k -truss communities to which the query vertex q belongs. We introduce a novel equivalence relation, k-truss equivalence , to model the intrinsic density and cohesiveness of edges in k -truss communities. Consequently, all the edges of G can be partitioned to a series of k -truss equivalence classes that constitute a space-efficient, truss-preserving index structure, EquiTruss. Community search can be henceforth addressed directly upon EquiTruss with...
Artur Abdullin Zubin Abraham Ibrahim Adeyanju Nagesh Adluru Muhaimenul Adnan Sara Aghakhani Rezwa... more Artur Abdullin Zubin Abraham Ibrahim Adeyanju Nagesh Adluru Muhaimenul Adnan Sara Aghakhani Rezwan Ahmed Reza Akbarinia Esra Akbas Abdulmohsen Algarni Nawaf Alkharoush Xiangdong An Periklis Andritsos Fabrizio Angiulli Yindalon Aphinyanaphongs Annalisa Appice Gowtham Atluri Alex Aved Ferhat Ay Nirmalya Bandyopadhyay Nicola Barbieri Satrajit Basu Montserrat Batet Kedar Bellare Dominik Benz Indrajit Bhattacharya Jiang Bian Wei Bian Hamad Binsalleeh julien Blanchard Petko Bogdanov Bo Cao Chen Cao Hong Cao Tianyu Cao Ruben ...
Journal of Homeland Security and Emergency Management
Emergent volunteer groups play a significant role during disasters. There is a rich literature on... more Emergent volunteer groups play a significant role during disasters. There is a rich literature on the role of volunteer groups in disasters and disaster volunteerism. However, the rapid proliferation of social media platforms in the last decade made a significant impact on human lives, and disaster volunteerism is no exception. This article argues that there is a need for understanding social media’s impact on disaster volunteerism. Using Harvey as a case, this article analyzes 74 Facebook groups that were created during the storm. The article compares the emergence and lifespan, structure, and function of online volunteer groups to those of volunteer groups before social media. Findings show important distinctions between online groups and those mentioned in the literature. First, online groups are easier to observe and analyze because of the digital traces they leave. Online groups emerge in different phases of disaster (response, early recovery) depending on people’s needs. Their...
2021 IEEE International Conference on Big Data (Big Data)
A drug-drug interaction (DDI) occurs when a drug is combined with other drug(s). DDIs have the po... more A drug-drug interaction (DDI) occurs when a drug is combined with other drug(s). DDIs have the potential to obstruct, increase, or diminish the intended impact of a drug or, in the worst-case scenario, induce an undesirable side effect. While it is critical to discover DDIs during clinical trials, it is impractical and expensive to detect all possible DDIs for a drug. Although several computational approaches for this problem have been developed, many of these methods need external biomedical knowledge that makes them difficult to generalize to drugs in early development phase. In this paper, we propose a novel method for predicting DDIs based on the vital chemical substructure of drugs extracted from their SMILES strings. We construct a graph that connects drugs based on their common functional chemical substructures. Furthermore, we apply different well-known graph neural network (GNN) methods to generate drug embeddings. Drug embeddings of individual drugs are concatenated to generate features of drug pairs. Finally, drug pair features are fed to different machine learning (ML) classifiers for DDI prediction. We evaluate our model on DrugBank dataset. Our result shows promising results and our model outperforms a baseline model based on different DDI representation creation methods.
ASPECT BASED OPINION MINING ON TURKISH TWEETS Esra AkbaÅŸ M.S. in Computer Engineering Supervisor:... more ASPECT BASED OPINION MINING ON TURKISH TWEETS Esra AkbaÅŸ M.S. in Computer Engineering Supervisor: Assoc. Prof. Dr. Hakan FerhatosmanoÄŸlu July, 2012 Understanding opinions about entities or brands is instrumental in reputation management and decision making. With the advent of social media, more people are willing to publicly share their recommendations and opinions. As the type and amount of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. Sentiment classification aims to identify the polarity of sentiment in text. The polarity is predicted on either a binary (positive, negative) or a multi-variant scale as the strength of sentiment expressed. Text often contains a mix of positive and negative sentiments, hence it is often necessary to detect both simultaneously. While classifying text based on sentiment polarity is a major task, analyzing sentiments separately for each aspect can be more useful in many applications...
We consider the community search problem defined upon a large graph G : given a query vertex q in... more We consider the community search problem defined upon a large graph G : given a query vertex q in G , to find as output all the densely connected subgraphs of G , each of which contains the query v . As an online, query-dependent variant of the well-known community detection problem, community search enables personalized community discovery that has found widely varying applications in real-world, large-scale graphs. In this paper, we study the community search problem in the truss-based model aimed at discovering all dense and cohesive k -truss communities to which the query vertex q belongs. We introduce a novel equivalence relation, k-truss equivalence , to model the intrinsic density and cohesiveness of edges in k -truss communities. Consequently, all the edges of G can be partitioned to a series of k -truss equivalence classes that constitute a space-efficient, truss-preserving index structure, EquiTruss. Community search can be henceforth addressed directly upon EquiTruss with...
Artur Abdullin Zubin Abraham Ibrahim Adeyanju Nagesh Adluru Muhaimenul Adnan Sara Aghakhani Rezwa... more Artur Abdullin Zubin Abraham Ibrahim Adeyanju Nagesh Adluru Muhaimenul Adnan Sara Aghakhani Rezwan Ahmed Reza Akbarinia Esra Akbas Abdulmohsen Algarni Nawaf Alkharoush Xiangdong An Periklis Andritsos Fabrizio Angiulli Yindalon Aphinyanaphongs Annalisa Appice Gowtham Atluri Alex Aved Ferhat Ay Nirmalya Bandyopadhyay Nicola Barbieri Satrajit Basu Montserrat Batet Kedar Bellare Dominik Benz Indrajit Bhattacharya Jiang Bian Wei Bian Hamad Binsalleeh julien Blanchard Petko Bogdanov Bo Cao Chen Cao Hong Cao Tianyu Cao Ruben ...
\textcopyright} 2017-IOS Press and the authors. Cells maintain cellular homeostasis employing dif... more \textcopyright} 2017-IOS Press and the authors. Cells maintain cellular homeostasis employing different regulatory mechanisms to respond external stimuli. We study two groups of signal-dependent transcriptional regulatory mechanisms. In the first group, we assume that repressor and activator proteins compete for binding to the same regulatory site on DNA (competitive mechanisms). In the second group, they can bind to different regulatory regions in a noncompetitive fashion (noncompetitive mechanisms). For both competitive and noncompetitive mechanisms, we studied the gene expression dynamics by increasing the repressor or decreasing the activator abundance (inhibition mechanisms), or by decreasing the repressor or increasing the activator abundance (activation mechanisms). We employed delay differential equation models. Our simulation results show that the competitive and noncompetitive inhibition mechanisms exhibit comparable repression effectiveness. However, response time is fastest in the noncompetitive inhibition mechanism due to increased repressor abundance, and slowest in the competitive inhibition mechanism by increased repressor level. The competitive and noncompetitive inhibition mechanisms through decreased activator abundance show comparable and moderate response times, while the competitive and noncompetitive activation mechanisms by increased activator protein level display more effective and faster response. Our study exemplifies the importance of mathematical modeling and computer simulation in the analysis of gene expression dynamics.
\textcopyright} 2016, Springer-Verlag Wien. Online social network analysis has attracted great at... more \textcopyright} 2016, Springer-Verlag Wien. Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender and age; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots versus humans; and entity matching on different social networks. We believe our summary of analysis of user characterization will provide important insights into researchers and better services to online users.
\textcopyright} 2017 Association for Computing Machinery. Graph clustering is a fundamental probl... more \textcopyright} 2017 Association for Computing Machinery. Graph clustering is a fundamental problem in social network analysis, the goal of which is to group vertices of a graph into a series of densely knitted clusters with each cluster well separated from all the others. Classical graph clustering methods take advantage of the graph topology to model and quantify vertex proximity. With the proliferation of rich graph contents, such as user profiles in social networks, and gene annotations in protein interaction networks, it is essential to consider both the structure and content information of graphs for high-quality graph clustering. In this paper, we propose a graph embedding approach to clustering content-enriched graphs. The key idea is to embed each vertex of a graph into a continuous vector space where the localized structural and attributive information of vertices can be encoded in a unified, latent representation. Specifically, we quantify vertex-wise attribute proximity into edge weights, and employ truncated, attribute-aware random walks to learn the latent representations for vertices. We evaluate our attribute-aware graph embedding method in real-world attributed graphs, and the results demonstrate its effectiveness in comparison with state-of-the-art algorithms.
As the type and the number of such venues increase, automated analysis of sentiment on textual re... more As the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has few resources for text mining. This approach would help enhance the sentiment analysis in languages where a list of opinionated words does not exist. We present a new method to automatically construct a list of words with their sentiment strengths. Then, we propose a new method that projects the text into dense and low dimensional feature vectors according to the sentiment strength of the words. We detect the mixture of positive and negative sentiments on a multi-variant scale. Empirical evaluation of the proposed framework on Turkish tweets shows that our approach gets good results for opinion mining.
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