Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Influence Maximization (IM), which selects a set of $k$ users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective , with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4983&context=sis_research

Influence Maximization on Social Graphs: A Survey

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind and the challenges of graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

https://dl.acm.org/doi/pdf/10.1145/3186727

Graph Summarization Methods and Applications: A Survey

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. 
Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.

Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

Modeling how information propagates in social networks driven by peer influence, is a fundamental research question towards understanding the structure and dynamics of these complex networks, as well as developing viral marketing applications Existing literature studies influence at the level of individuals, mostly ignoring the existence of a community structure in which multiple nodes may exhibit a common influence pattern

In this paper we introduce CSI, a model for analyzing information propagation and social influence at the granularity of communities CSI builds over a novel propagation model that generalizes the classic Independent Cascade model to deal with groups of nodes instead of single nodes influence Given a social network and a database of past information propagation, we propose a hierarchical approach to detect a set of communities and their reciprocal influence strength CSI provides a higher level and more intuitive description of the influence dynamics, thus representing a powerful tool to summarize and investigate patterns of influence in large social networks The evaluation on various datasets suggests the effectiveness of the proposed approach in modeling information propagation at the level of communities It further enables to detect interesting patterns of influence, such as the communities that play a key role in the overall diffusion process, or that are likely to start information cascades

/pdf/csi-community-level-social-influence-analysis-1p0vwp5guf.pdf

CSI: Community-Level Social Influence Analysis

In this article, we present an analysis of the impact of nutrition and lifestyle on health at a global level. We have used Self-organizing Maps (SOM) algorithm as the analysis technique. SOM enables us to visualize the relative position of each country against a set of the variables related to nutrition, lifestyle and health. The positioning of the countries follows the basic understanding of their status with respect to their socioeconomic conditions. We have also studied the relationships between the variables supported by the SOM visualization. This analysis presents many obvious correlations but also some surprising findings that are worth further analyses.

/pdf/self-organizing-maps-of-nutrition-lifestyle-and-health-of0429vkeh.pdf

Self-organizing maps of nutrition, lifestyle and health situation in the world

What is the set of nodes of a social network that, under a probabilistic contagion model, would get infected if a given node $s$ gets infected? We call this set the sphere of influence of s. Due to the stochastic nature of the contagion model we need to define a notion of "expected" or "typical" cascade: this is a set of nodes which is the closest to all the possible cascades starting from s. We thus formalize the Typical Cascade problem which requires, for a given source node s, to find the set of nodes minimizing the expected Jaccard distance to all the possible cascades from s. The expected cost of a typical cascade also provides us a measure of the stability of cascade propagation, i.e., how much random cascades from a source node s deviate from the "typical" cascade. In this sense source nodes with lower expected costs are more reliable. We show that, while computing the quality of a candidate solution is SPhard, a method based on (1) sampling random cascades and (2) computing their Jaccard Median, can obtain a multiplicative approximation with just O(1) samples. We then devise an index that allows to efficiently compute the sphere of influence for any node in the network. Finally, we propose to approach the influence maximization problem as an instance of set cover on the spheres of influence. Through exhaustive evaluation using real-world networks and different methods of assigning the influence probability to each edge, we show that our approach outperforms in quality the theoretically optimal greedy algorithm.

Spheres of Influence for More Effective Viral Marketing

Understanding the dynamics underlying the diffusion of new ideas or technology in a society is an important task with implications for sciences such sociology and economics, as well as important business applications, especially in marketing. In this article, we take a first step in this direction, by studying the problem of how to model, in a simple and useful abstraction, the complex process of innovation diffusion. Our unique input is a database of adoptions $$\mathbb {D}$$D, which is a relation (User,Item,Time) where a tuple $${\left\langle u, i, t\right\rangle } \in \mathbb {D}$$u,i,tźD indicates that the user u adopted the item i at time t. For our aim, we propose a stochastic model which decomposes a diffusion trace (i.e., the sequence of adoptions of the same item i) in an ordered sequence of stages, where each stage is intuitively built around two dimensions: users and relative speed at which adoptions happen. Each stage is characterized by a specific rate of adoption and it involves different users to different extent, while the sequentiality in the diffusion is guaranteed by constraining the transition probabilities among stages. An empirical evaluation on synthetic and real-world adoption datasets shows the effectiveness of the proposed framework in summarizing the adoption process, enabling several analysis tasks such as the identification of adopter categories, clustering and characterization of diffusion traces, and prediction of which users will adopt an item in the next future.

/pdf/modeling-adoptions-and-the-stages-of-the-diffusion-of-3hmznz14ge.pdf

Modeling adoptions and the stages of the diffusion of innovations

By abstracting Grid middleware specific considerations from clinical research applications, re-usable services should be developed that will provide generic functionality aimed specifically at medical applications. In the scope of the neuGRID project, generic services are being designed and developed which will be applied to satisfy the requirements of neuroscientists. These services will bring together sources of data and computing elements into a single view as far as applications are concerned, making it possible to cope with centralised, distributed or hybrid data and provide native support for common medical file formats. Services will include querying, provenance, portal, anonymization and pipeline services together with a 'glueing' service for connection to Grid services. Thus lower-level services will hide the peculiarities of any specific Grid technology from upper layers, provide application independence and will enable the selection of 'fit-for-purpose' infrastructures. This paper outlines the design strategy being followed in neuGRID using the glueing and pipeline services as examples.

Yasir Mehmood

Papers

CSI: Community-Level Social Influence Analysis

Self-organizing maps of nutrition, lifestyle and health situation in the world

Spheres of Influence for More Effective Viral Marketing

Modeling adoptions and the stages of the diffusion of innovations

Reusable Services from the neuGRID Project for Grid-Based Health Applications