Disambiguation of Large-Scale Educational Network Data for Social Network Analysis

Social Network Analysis (SNA) is a research method which quantitatively maps qualitative social interactions between individuals who comprise a ‘network’. Delineating these social networks yields highly valuable data, including SNA measures like centrality, which can be used to measure social influence, connectivity, and more. Further, these networks can also be visualized by graphing individuals as ‘nodes’, and then by drawing ‘edges’ (the lines that connect them) to produce sociograms. With these sociograms, researchers can concurrently conduct visual and statistical analysis of relations between node and network traits of interest. As a result of these capabilities and the growth of social learning, SNA has become increasingly popular in educational settings. However, the difficulties in consolidating students’ interaction data into quantitative networks has steered SNA researchers towards oversimplified social environments which do not exhibit ambiguous connections. For example, research is well established in observing students’ online interactions, where participant information is collected concurrent with interaction data. Similarly, SNA studies in face-to-face contexts are typically bounded to single classrooms which greatly reduces the number of participants’ possible ties. These examples observe environments that are easily monitored, and bar the observation of the true underlying social networks. Hence, a gap exists for those hoping to understand the true, non-course-bounded networks of undergraduate students. To this end, our research group is currently conducting a study comparing all participating freshmen and sophomore engineering students’ interactions to academic outcomes at USU using social network analysis. The current disambiguation process for this large (1000+ nodes), ambiguous (open response name identified ties), interaction data is manually intensive, intricate, and takes careful organization--increasing with network size. Therefore, charged with the task of interaction data disambiguation, I have organized the overarching disambiguation task into a hybrid blend of automation and manual stages, to take advantage of emerging network information throughout the process (i.e., previously ambiguous responses are connected to an entity). The procedural analysis I present in this poster then highlights the technicalities of these stages, which begin with simple spelling checks, and end with a sub-network comparison process (similar to agglomerative hierarchical clustering) to yield accurate and complete network data. My methodology proved effective in matching many ambiguous names with their counterparts found elsewhere in the network. However, several names were still unable to be consolidated and had to be de-identified in their present conditions. Therefore, this presentation also highlights procedures that could be refined through modern SNA clustering methods, including possibilities for defining supervised algorithm parameters and comparing our manual to automated results for such algorithms. As the need for understanding the relationship between interpersonal interactions and educational outcomes expands, so too are the needs for improved SNA methods. To meet these growing needs, researchers must develop new and effective procedures for the disambiguation of authentic interaction data. This presentation provides an example of such research, developing and disseminating more effective and efficient approaches for network development.https://digitalcommons.usu.edu/fsrs2021/1005/thumbnail.jp

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Multi-Scale Link Prediction

The automated analysis of social networks has become an important problem due to the proliferation of social networks, such as LiveJournal, Flickr and Facebook. The scale of these social networks is massive and continues to grow rapidly. An important problem in social network analysis is proximity estimation that infers the closeness of different users. Link prediction, in turn, is an important application of proximity estimation. However, many methods for computing proximity measures have high computational complexity and are thus prohibitive for large-scale link prediction problems. One way to address this problem is to estimate proximity measures via low-rank approximation. However, a single low-rank approximation may not be sufficient to represent the behavior of the entire network. In this paper, we propose Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can handle massive networks. The basis idea of MSLP is to construct low rank approximations of the network at multiple scales in an efficient manner. Based on this approach, MSLP combines predictions at multiple scales to make robust and accurate predictions. Experimental results on real-life datasets with more than a million nodes show the superior performance and scalability of our method.Comment: 20 pages, 10 figure

X-Vine: Secure and Pseudonymous Routing Using Social Networks

Distributed hash tables suffer from several security and privacy vulnerabilities, including the problem of Sybil attacks. Existing social network-based solutions to mitigate the Sybil attacks in DHT routing have a high state requirement and do not provide an adequate level of privacy. For instance, such techniques require a user to reveal their social network contacts. We design X-Vine, a protection mechanism for distributed hash tables that operates entirely by communicating over social network links. As with traditional peer-to-peer systems, X-Vine provides robustness, scalability, and a platform for innovation. The use of social network links for communication helps protect participant privacy and adds a new dimension of trust absent from previous designs. X-Vine is resilient to denial of service via Sybil attacks, and in fact is the first Sybil defense that requires only a logarithmic amount of state per node, making it suitable for large-scale and dynamic settings. X-Vine also helps protect the privacy of users social network contacts and keeps their IP addresses hidden from those outside of their social circle, providing a basis for pseudonymous communication. We first evaluate our design with analysis and simulations, using several real world large-scale social networking topologies. We show that the constraints of X-Vine allow the insertion of only a logarithmic number of Sybil identities per attack edge; we show this mitigates the impact of malicious attacks while not affecting the performance of honest nodes. Moreover, our algorithms are efficient, maintain low stretch, and avoid hot spots in the network. We validate our design with a PlanetLab implementation and a Facebook plugin.Comment: 15 page

On the discovery of social roles in large scale social systems

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems

Community Graph Sequence with Sequence Data of Network Structured Data

Recently, there has been increasing interest in data analysis for network structured data. The network structured data is represented the relation between one data and other data by graph structure. There are many network structured data such as social networks, biological networks in the real world. In this study, we will analysis the network structured data that has dynamic relation and complex interact with each data. And, we will approach the problem that is to extract transition pattern from the history of temporal change in their network structured data. Especially, in this paper, we will apply community graph sequences to graph sequences of network structured data that has large-scale and complex changes, and propose the method of extracting transition pattern of network structured data. We used social bookmark data as the data streams of analysis object and verified that social bookmark data is the network structured data that has large-scale and complex change