STWalk: Learning Trajectory Representations in Temporal Graphs

Analyzing the temporal behavior of nodes in time-varying graphs is useful for many applications such as targeted advertising, community evolution and outlier detection. In this paper, we present a novel approach, STWalk, for learning trajectory representations of nodes in temporal graphs. The proposed framework makes use of structural properties of graphs at current and previous time-steps to learn effective node trajectory representations. STWalk performs random walks on a graph at a given time step (called space-walk) as well as on graphs from past time-steps (called time-walk) to capture the spatio-temporal behavior of nodes. We propose two variants of STWalk to learn trajectory representations. In one algorithm, we perform space-walk and time-walk as part of a single step. In the other variant, we perform space-walk and time-walk separately and combine the learned representations to get the final trajectory embedding. Extensive experiments on three real-world temporal graph datasets validate the effectiveness of the learned representations when compared to three baseline methods. We also show the goodness of the learned trajectory embeddings for change point detection, as well as demonstrate that arithmetic operations on these trajectory representations yield interesting and interpretable results.Comment: 10 pages, 5 figures, 2 table

LASAGNE: Locality And Structure Aware Graph Node Embedding

In this work we propose Lasagne, a methodology to learn locality and structure aware graph node embeddings in an unsupervised way. In particular, we show that the performance of existing random-walk based approaches depends strongly on the structural properties of the graph, e.g., the size of the graph, whether the graph has a flat or upward-sloping Network Community Profile (NCP), whether the graph is expander-like, whether the classes of interest are more k-core-like or more peripheral, etc. For larger graphs with flat NCPs that are strongly expander-like, existing methods lead to random walks that expand rapidly, touching many dissimilar nodes, thereby leading to lower-quality vector representations that are less useful for downstream tasks. Rather than relying on global random walks or neighbors within fixed hop distances, Lasagne exploits strongly local Approximate Personalized PageRank stationary distributions to more precisely engineer local information into node embeddings. This leads, in particular, to more meaningful and more useful vector representations of nodes in poorly-structured graphs. We show that Lasagne leads to significant improvement in downstream multi-label classification for larger graphs with flat NCPs, that it is comparable for smaller graphs with upward-sloping NCPs, and that is comparable to existing methods for link prediction tasks

Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

Social Media Analytics using Data Mining

There is a rapid increase in the usage of social media in the most recent decade. Getting to social media platforms for example, Twitter, Facebook LinkedIn and Google+ via mediums like web and the web 2.0 has become the most convenient way for users. Individuals are turning out to be more inspired by and depending on such platforms for data, news and thoughts of different clients on various topics. The substantial dependence on these social platforms causes them to produce huge information described by three computational issues in particular; volume, velocity and dynamism. These issues frequently make informal organization information exceptionally complex to break down physically, bringing about the related utilization of computational method for dissecting them. Information mining gives an extensive variety of strategies for recognizing valuable information from huge datasets like patterns, examples and standards. Various data mining strategies are utilized for useful data recovery, factual displaying and machine learning. These systems generally do a sort of pre-processing of data, performs the data analysis and information. This study examines distinctive information mining procedures utilized as a part of mining different parts of the informal community over decades going from the chronicled systems to the forward model

Historically black colleges and university libraries’ utilization of Twitter for patron engagement: an exploratory study

Historically Black College and University (HBCU) libraries have incorporated the micro-blogging service Twitter into their information services as a strategy to market and inform library users. However, there is little in the literature on assessment; do we know if users are interacting with libraries via social media? This study examined followers of HBCU libraries, and measures their engagement with library-generated content on Twitter. This study utilizes social analytics techniques, specifically propagation and sentiment analysis to measure the state of engagement among library Twitter followers, within a one-year period. Dispute an active presence on Twitter; libraries in this investigation had a relativity small footprint in the Twitter universe. Results indicate little engagement with followers and neutral emotional responses to library-generated tweets

A genetic algorithm-based approach to mapping the diversity of networks sharing a given degree distribution and global clustering

The structure of a network plays a key role in the outcome of dynamical processes operating on it. Two prevalent network descriptors are the degree distribution and the global clustering. However, when generating networks with a prescribed degree distribution and global clustering, it has been shown that changes in structural properties other than that controlled for are induced and these changes have been found to alter the outcome of spreading processes on the network. This therefore begs the question of our understanding of the potential diversity of networks sharing a given degree distribution and global clustering. As the space of all possible networks is too large to be systematically explored, a heuristic approach is needed. In our genetic algorithm-based approach, networks are encoded by their subgraph counts from a chosen family of subgraphs. Coverage of the space of possible networks is then maximised by focusing the search through optimising the diversity of counts by the Map-Elite algorithm. We provide preliminary evidence of our approach’s ability to sample from the space of possible networks more widely than some state of the art methods

Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion Mining

In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides  balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact

Formatting Data for One and Two Mode Undirected Social Network Analysis

Social Network Analysis (SNA) is a statistical method used to analyze the social structure and interactions among individuals within a network. SNA is used extensively in a number of disciplines such as sociology, geography, and communications research. However, the use of SNA by practitioners and researchers in assessment and evaluation is much lower than their counterparts in other social science disciplines. One of the primary barriers to utilizing SNA in social science research is correctly formatting the data for use. The focus of this article is to provide researchers with a tool for restructuring long form data so that it can be used to conduct social network analyses and generate undirected sociograms