Multi-dimensional Conversation Analysis across Online Social Networks

With the advance of the Internet, ordinary users have created multiple personal accounts on online social networks, and interactions among these social network users have recently been tagged with location information. In this work, we observe user interactions across two popular online social networks, Facebook and Twitter, and analyze which factors lead to retweet/like interactions for tweets/posts. In addition to the named entities, lexical errors and expressed sentiments in these data items, we also consider the impact of shared user locations on user interactions. In particular, we show that geolocations of users can greatly affect which social network post/tweet will be liked/ retweeted. We believe that the results of our analysis can help researchers to understand which social network content will have better visibility.Comment: Datasets will be anonymized and published at: http://akcora.wordpress.com/2013/12/24/pointer-for-datasets

Risks of Friendships on Social Networks

In this paper, we explore the risks of friends in social networks caused by their friendship patterns, by using real life social network data and starting from a previously defined risk model. Particularly, we observe that risks of friendships can be mined by analyzing users' attitude towards friends of friends. This allows us to give new insights into friendship and risk dynamics on social networks.Comment: 10 pages, 8 figures, 3 tables. To Appear in the 2012 IEEE International Conference on Data Mining (ICDM

Blockchain: A Graph Primer

Bitcoin and its underlying technology Blockchain have become popular in recent years. Designed to facilitate a secure distributed platform without central authorities, Blockchain is heralded as a paradigm that will be as powerful as Big Data, Cloud Computing and Machine learning. Blockchain incorporates novel ideas from various fields such as public key encryption and distributed systems. As such, a reader often comes across resources that explain the Blockchain technology from a certain perspective only, leaving the reader with more questions than before. We will offer a holistic view on Blockchain. Starting with a brief history, we will give the building blocks of Blockchain, and explain their interactions. As graph mining has become a major part its analysis, we will elaborate on graph theoretical aspects of the Blockchain technology. We also devote a section to the future of Blockchain and explain how extensions like Smart Contracts and De-centralized Autonomous Organizations will function. Without assuming any reader expertise, our aim is to provide a concise but complete description of the Blockchain technology.Comment: 16 pages, 8 figure

Profiling user interactions on online social networks.

Over the last couple of years, there has been signi_cant research e_ort in mining user behavior on online social networks for applications ranging from sentiment analysis to marketing. In most of those applications, usually a snapshot of user attributes or user relationships are analyzed to build the data mining models, without considering how user attributes and user relationships can be utilized together. In this thesis, we will describe how user relationships within a social network can be further augmented by information gathered from user generated texts to analyze large scale dynamics of social networks. Speci_cally, we aim at explaining social network interactions by using information gleaned from friendships, pro_les, and status posts of users. Our approach pro_les user interactions in terms of shared similarities among users, and applies the gained knowledge to help users in understanding the inherent reasons, consequences and bene_ts of interacting with other social network users

Profiling user interactions on online social networks.

Over the last couple of years, there has been signi_cant research e_ort in mining user behavior on online social networks for applications ranging from sentiment analysis to marketing. In most of those applications, usually a snapshot of user attributes or user relationships are analyzed to build the data mining models, without considering how user attributes and user relationships can be utilized together. In this thesis, we will describe how user relationships within a social network can be further augmented by information gathered from user generated texts to analyze large scale dynamics of social networks. Speci_cally, we aim at explaining social network interactions by using information gleaned from friendships, pro_les, and status posts of users. Our approach pro_les user interactions in terms of shared similarities among users, and applies the gained knowledge to help users in understanding the inherent reasons, consequences and bene_ts of interacting with other social network users

Synthesis and Characterization of Diblock Copolymer Templated Iron Oxide Nanoparticles

Templating ordered assemblies of magnetic oxide nanoparticles within self-assembled diblock copolymers of varying morphologies is an important problem with a wide applicability such as in electromagnetics, optical devices, metal catalysts, medicine and biology. In this thesis, the effects of different polymer structures on particle ordering and resultant magnetic properties have been investigated using various microstructure and magnetic characterization tools. Ring-opening metathesis polymerization (ROMP) of norbornene and functionalized norbornene monomers has been used to synthesize diblock copolymers of narrow polydispersities using Grubbs' catalyst. These block copolymers can be used as templates to form inorganic nanoparticles. In this research, the structural and physical understanding of the inorganic-copolymer system was studied by small-angle neutron and x-ray scattering techniques and transmission electron microscopy. Synthesis of $\gamma$-Fe$_2$O$_3$ nanoparticles has been achieved within novel block copolymers of (norbornene)-b-(deuterated norbornene dicarboxylic) acid and (norbornene methanol)-(norbornene dicarboxylic acid). The polymer morphologies were controlled by varying the volume fractions of the constituent blocks. The pure norbornene based diblock copolymer morphologies were demonstrated by electron microscopy for the first time. Spherical, cylindrical and lamellar morphologies of these novel diblock copolymers were reported. The block ratios of the synthesized polymers were determined using gel permeation chromatography - light scattering, elemental analysis and UV-VIS spectroscopy. Solution phase doping and submersion of thin films in metal salt solutions were employed as metal doping methods and the observed nanoparticle structures were compared to those of the undoped copolymer morphologies. This project reports on the types of templating structures and dispersion of the nanoparticles. The effects of particle interactions on the microphase separation and magnetic properties were also investigated. The knowledge gained from understanding the templating mechanism in block copolymer / iron oxide nanocomposites can be applied to other similar systems for a variety of biological and catalyst applications

Tuning the Mechanical Properties in Model Nanocomposites: Influence of the Polymer-Filler Interfacial Interactions

This paper presents a study of the polymer-filler interfacial effects on filler dispersion and mechanical reinforcement in Polystyrene (PS) / silica nanocomposites by direct comparison of two model systems: un-grafted and PS-grafted silica dispersed in PS matrix. The structure of nanoparticles has been investigated by combining Small Angle Neutron Scattering (SANS) measurements and Transmission Electronic Microscopic (TEM) images. The mechanical properties were studied over a wide range of deformation by plate/plate rheology and uni-axial stretching. At low silica volume fraction, the particles arrange, for both systems, in small finite size non-connected aggregates and the materials exhibit a solid-like behavior independent of the local polymer/fillers interactions suggesting that reinforcement is dominated by additional long range effects. At high silica volume fraction, a continuous connected network is created leading to a fast increase of reinforcement whose amplitude is then directly dependent on the strength of the local particle/particle interactions and lower with grafting likely due to deformation of grafted polymer.Comment: Journal Polymer Science (2011