"How can we summarize large graphs of different types, e.g., unipartite or bipartite, directed or undirected? How can we find anomalous patterns in such graphs efficiently? In this paper we present PERSEUS3, a large-scale graph mining system that supports analysis of three types of graphs: unipartite and undirected; bipartite and undirected; and unipartite and directed. Our system provides coupled summarization of graph properties and the network structure, and allows the user to interactively explore normal and anomalous node behaviors.

PERSEUS3 is developed based on PERSEUS [7] with three significant extensions: (1) Graph statistics are extracted depending on the type of the input network (e.g., total degree, eigenvectors for undirected graphs; in/out degree, SVD vectors for directed graphs); (2) Subgraphs of the selected node are interactively visualized through the adjacency matrices; (3) Heatmaps (instead of simple scatterplots) are adopted in graph summarization to improve the scalability of the system.

Our extensive experiments show that PERSEUS3 handles different tasks of graph mining efficiently. Specifically we run the univariate undirected graph analysis on a Twitter who-follows-whom graph which spans 0.26 million users and 220 million links; we also run the bipartite graph analysis on a user-movie ratings dataset, and the directed graph analysis on a patent citation graph. We report the patterns discovered, including bipartite cores and outliers spotted by PERSEUS3."

"Given a large graph with several millions or billions of nodes and edges, such as a social network, how can we explore it eciently and find out what is in the data? In this demo we present Perseus, a large-scale system that enables the comprehensive analysis of large graphs by supporting the coupled summarization of graph properties and structures, guiding attention to outliers, and allowing the user to interactively explore normal and anomalous node behaviors. Specifically, Perseus provides for the following operations: 1) It automatically extracts graph invariants (e.g., degree, PageRank, real eigenvectors) by performing scal-able, o✏ine batch processing on Hadoop; 2) It interactively visualizes univariate and bivariate distributions for those in-variants; 3) It summarizes the properties of the nodes that the user selects; 4) It eciently visualizes the induced sub-graph of a selected node and its neighbors, by incrementally revealing its neighbors. In our demonstration, we invite the audience to interact with Perseus to explore a variety of multi-million-edge social networks including a Wikipedia vote network, a friend-ship/foeship network in Slashdot, and a trust network based on the consumer review website Epinions.com."

"Mining massive datasets can benefit from human input, but current approaches require making tradeoffs between overburdening end users or under-informing the system – algorithms become more accurate given more training data, but requiring more exemplars takes significant user effort. In this paper, we suggest an approach that engages non-expert and semi-expert crowds as a supporting " interface layer " between end users and data mining systems. Lever-aging human intelligence will allow systems to answer new types of queries (e.g., vague or subjective ones) and generate richer example sets for user-specified patterns. Using crowdsourcing to parallelize this task makes it possible to provide training data to the system in nearly real-time. This allows the system to learn from crowd-generated examples of user-provided instances within the span of a single query. The user can also post follow-up queries to iteratively refine results. By maintaining an ongoing context for these interactions, we can make the query-respond-refine process resemble a " conversational " interaction between user and system, helping make data analysis more approachable to non-experts."

"How can we visualize billion-scale graphs? How to spot outliers in such graphs quickly? Visualizing graphs is the most direct way of understanding them; however, billion-scale graphs are very difficult to visualize since the amount of information overflows the resolution of a typical screen. In this paper we propose NET-RAY, an open-source package for visualizationbased mining on billion-scale graphs. NET-RAY visualizes graphs using the spy plot (adjacency matrix patterns), distribution plot, and correlation plot which involve careful node ordering and scaling. In addition, NET-RAY efficiently summarizes scatter clusters of graphs in a way that finds outliers automatically, and makes it easy to interpret them visually. Extensive experiments show that NET-RAY handles very large graphs with billions of nodes and edges efficiently and effectively. Specifically, among the various datasets that we study, we visualize in multiple ways the YahooWeb graph which spans 1.4 billion webpages and 6.6 billion links, and the Twitter whofollows-whom graph, which consists of 62.5 million users and 1.8 billion edges. We report interesting clusters and outliers spotted and summarized by NET-RAY."

"How much has a network changed since yesterday? How different is the wiring of Bob's brain (a left-handed male) and Alice's brain (a right-handed female), and how is it different? Graph similarity with given node correspondence, i.e., the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DELTACON, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g., employees of a company, customers of a mobile carrier). In conjunction, we propose DELTACON-ATTR, a related approach that enables attribution of change or dissimilarity to responsible nodes and edges. Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ DELTACON and DELTACON-ATTR on real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, (b) do temporal anomaly detection in the who-emails-whom Enron graph and find the top culprits for the changes in the temporal corporate email graph, and (c) recover pairs of test-retest large brain scans (∼17M edges, up to 90M edges) for 21 subjects."

"How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customers of a mobile carrier). Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph."

"How is the human brain wired? Are there differences between, say, males and females or people with high math skills and normal people? Recently, there has been an upsurge of interest in connectomics - the field that focuses on neural imaging techniques and brain connectivity maps. The production of connectomes (maps of neural connections in the brain) has led to the need of analyzing in order to better understand how the brain functions.

Connectomes are commonly represented as graphs, a structure that consists of nodes (voxels) and edges between them (connections representing activity between the voxels). Despite the interest in the properties of the connectomes (brain graphs), to the best of our knowledge, the existing works focus on small brain graphs with <100 voxels. In this work, we study mainly large connectomes, that consist of over 650K nodes and 1.3M edges.

Our contributions are the following:
- We study large connectomes of size 0.2-0.3GB, which have never been studied before in the literature.
- We apply scalable, state-of-the-art graph-mining methods to study the topology of the connectomes.
- We report our findings."

"It has been suggested that online search and retrieval contributes to the intellectual isolation of users within their preexisting ideologies, where people's prior views are strengthened and alternative viewpoints are infrequently encountered. This so-called "filter bubble" phenomenon has been called out as especially detrimental when it comes to dialog among people on controversial, emotionally charged topics, such as the labeling of genetically modified food, the right to bear arms, the death penalty, and online privacy. We seek to identify and study information-seeking behavior and access to alternative versus reinforcing viewpoints following shocking, emotional, and large-scale news events. We choose for a case study to analyze search and browsing on gun control/rights, a strongly polarizing topic for both citizens and leaders of the United States. We study the period of time preceding and following a mass shooting to understand how its occurrence, follow-on discussions, and debate may have been linked to changes in the patterns of searching and browsing. We employ information-theoretic measures to quantify the diversity of Web domains of interest to users and understand the browsing patterns of users. We use these measures to characterize the influence of news events on these web search and browsing patterns."

"How do people interact with their Facebook wall? At a high level, this question captures the essence of our work. While most prior efforts focus on Twitter, the much fewer Facebook studies focus on the friendship graph or are limited by the amount of users or the duration of the study. In this work, we model Facebook user behavior: we analyze the wall activities of users focusing on identifying common patterns and surprising phenomena. We conduct an extensive study of roughly 7K users over three years during four month intervals each year. We propose PowerWall, a lesser known heavy-tailed distribution to fit our data. Our key results can be summarized in the following points. First, we find that many wall activities, including number of posts, number of likes, number of posts of type photo, etc., can be described by the PowerWall distribution. What is more surprising is that most of these distributions have similar slope, with a value close to 1! Second, we show how our patterns and metrics can help us spot surprising behaviors and anomalies. For example, we find a user posting every two days, exactly the same count of posts; another user posting at midnight, with no other activity before or after. Our work provides a solid step towards a systematic and quantitative wall-centric profiling of Facebook user activity."