Opinion dynamics with varying susceptibility to persuasion

A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider interventions that modify people's susceptibility to persuasion. In this work, we adopt a popular model for social opinion dynamics, and we formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. We show that modeling interventions at the level of susceptibility lead to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization and show its efficacy at finding target-sets that optimize the sum of opinions compared on real world networks, including a Twitter network with real opinion estimates

Graph based Anomaly Detection and Description: A Survey

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the ‘why’, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field

Feature reduction improves classification accuracy in healthcare

Our work focuses on inductive transfer learning, a setting in which one assumes that both source and target tasks share the same features and label spaces. We demonstrate that transfer learning can be successfully used for feature reduction and hence for more efficient classification performance. Further, our experiments show that this approach increases the precision of the classification task as well

Seeding with Costly Network Information

We study the task of selecting $k$ nodes in a social network of size $n$, to seed a diffusion with maximum expected spread size, under the independent cascade model with cascade probability $p$. Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed set with provable guarantees, given the knowledge of the entire network. However, in practice, obtaining full knowledge of the network is very costly. To address this gap, we first study the achievable guarantees using $o(n)$ influence samples. We provide an approximation algorithm with a tight (1-1/e){\mbox{OPT}}-\epsilon n guarantee, using $O_{\epsilon}(k^2\log n)$ influence samples and show that this dependence on $k$ is asymptotically optimal. We then propose a probing algorithm that queries ${O}_{\epsilon}(p n^2\log^4 n + \sqrt{k p} n^{1.5}\log^{5.5} n + k n\log^{3.5}{n})$ edges from the graph and use them to find a seed set with the same almost tight approximation guarantee. We also provide a matching (up to logarithmic factors) lower-bound on the required number of edges. To address the dependence of our probing algorithm on the independent cascade probability $p$, we show that it is impossible to maintain the same approximation guarantees by controlling the discrepancy between the probing and seeding cascade probabilities. Instead, we propose to down-sample the probed edges to match the seeding cascade probability, provided that it does not exceed that of probing. Finally, we test our algorithms on real world data to quantify the trade-off between the cost of obtaining more refined network information and the benefit of the added information for guiding improved seeding strategies

Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs

Graphs are an important tool to model data in different domains, including social networks, bioinformatics and the world wide web. Most of the networks formed in these domains are directed graphs, where all the edges have a direction and they are not symmetric. Betweenness centrality is an important index widely used to analyze networks. In this paper, first given a directed network $G$ and a vertex $r \in V(G)$, we propose a new exact algorithm to compute betweenness score of $r$. Our algorithm pre-computes a set $\mathcal{RV}(r)$, which is used to prune a huge amount of computations that do not contribute in the betweenness score of $r$. Time complexity of our exact algorithm depends on $|\mathcal{RV}(r)|$ and it is respectively $\Theta(|\mathcal{RV}(r)|\cdot|E(G)|)$ and $\Theta(|\mathcal{RV}(r)|\cdot|E(G)|+|\mathcal{RV}(r)|\cdot|V(G)|\log |V(G)|)$ for unweighted graphs and weighted graphs with positive weights. $|\mathcal{RV}(r)|$ is bounded from above by $|V(G)|-1$ and in most cases, it is a small constant. Then, for the cases where $\mathcal{RV}(r)$ is large, we present a simple randomized algorithm that samples from $\mathcal{RV}(r)$ and performs computations for only the sampled elements. We show that this algorithm provides an $(\epsilon,\delta)$-approximation of the betweenness score of $r$. Finally, we perform extensive experiments over several real-world datasets from different domains for several randomly chosen vertices as well as for the vertices with the highest betweenness scores. Our experiments reveal that in most cases, our algorithm significantly outperforms the most efficient existing randomized algorithms, in terms of both running time and accuracy. Our experiments also show that our proposed algorithm computes betweenness scores of all vertices in the sets of sizes 5, 10 and 15, much faster and more accurate than the most efficient existing algorithms.Comment: arXiv admin note: text overlap with arXiv:1704.0735

Mining high utility sequential patterns

University of Technology Sydney. Faculty of Engineering and Information Technology.Sequential pattern mining refers to the identification of frequent subsequences in sequence databases as patterns. It provides an effective way to analyze the sequential data. The selection of interesting sequences is generally based on the frequency/support framework: sequences of high frequency are treated as significant. In the last two decades, researchers have proposed many techniques and algorithms for extracting the frequent sequential patterns, in which the downward closure property (also known as Apriori property) plays a fundamental role. At the same time, the relative importance of each item has been introduced in frequent pattern mining, and “high utility itemset mining” has been proposed. Instead of selecting high frequency patterns, the utility-based methods extract itemsets with high utilities, and many algorithms and strategies have been proposed. These methods can only process the itemsets in the utility framework. However, all the above methods suffer from the following common issues and problems to varying extents: 1) Sometimes, most of frequent patterns may not be informative to business decision-making, since they do not show the business value and impact. 2) Even if there is an algorithm that considers the business impact (namely utility), it can only obtain high utility sequences based on a given minimum utility threshold, thus it is very difficult for users to specify an appropriate minimum utility and to directly obtain the most valuable patterns. 3) The algorithm in the utility framework may generate a large number of patterns, many of which maybe redundant. Although high utility sequential pattern mining is essential, discovering the patterns is challenging for the following reasons: 1) The downward closure property does not hold in utility-based sequence mining. This means that most of the existing algorithms cannot be directly transferred, e.g. from frequent sequential pattern mining to high utility sequential pattern mining. Furthermore, compared to high utility itemset mining, utility-based sequence analysis faces the critical combinational explosion and computational complexity caused by sequencing between sequential elements (itemsets). 2) Since the minimum utility is not given in advance, the algorithm essentially starts searching from 0 minimum support. This not only incurs very high computational costs, but also the challenge of how to raise the minimum threshold without missing any top-k high utility sequences. 3) Due to the fundamental difference, incorporating the traditional closure concept into high utility sequential pattern mining makes the outcome patterns irreversibly lossy and no longer recoverable, which will be reasoned in the following chapters. Therefore, it is exceedingly challenging to address the above issues by designing a novel representation for high utility sequential patterns. To address these research limitations and challenges, this thesis proposes a high utility sequential pattern mining framework, and proposes both a threshold-based and top-k-based mining algorithm. Furthermore, a compact and lossless representation of utility-based sequence is presented, and an efficient algorithm is provided to mine such kind of patterns. Chapter 2 thoroughly reviews the related works in the frequent sequential pattern mining and high utility itemset/sequence mining. Chapter 3 incorporates utility into sequential pattern mining, and a generic framework for high utility sequence mining is defined. Two efficient algorithms, namely USpan and USpan+, are presented to mine for high utility sequential patterns. In USpan and USpan+, we introduce the lexicographic quantitative sequence tree to extract the complete set of high utility sequences and design concatenation mechanisms for calculating the utility of a node and its children with three effective pruning strategies. Chapter 4 proposes a novel framework called top-k high utility sequential pattern mining to tackle this critical problem. Accordingly, an efficient algorithm, Top-k high Utility Sequence (TUS for short) mining, is designed to identify top-k high utility sequential patterns without minimum utility. In addition, three effective features are introduced to handle the efficiency problem, including two strategies for raising the threshold and one pruning for filtering unpromising items. Chapter 5 proposes a novel concise framework to discover US-closed (Utility Sequence closed) high utility sequential patterns, with theoretical proof that it expresses the lossless representation of high-utility patterns. An efficient algorithm named CloUSpan is introduced to extract the US-closed patterns. Two effective strategies are used to enhance the performance of CloUSpan. All of the algorithms are examined in both synthetic and real datasets. The performances, including the running time and memory consumption, are compared. Furthermore, the utility-based sequential patterns are compared with the patterns in the frequency/support framework. The results show that high utility sequential patterns provide insightful knowledge for users