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Cascading Behavior in Large Blog Graphs [article]

Jure Leskovec, Mary McGlohon, Christos Faloutsos, Natalie Glance, Matthew Hurst
2007 arXiv   pre-print
How do blogs cite and influence each other? How do such links evolve? Does the popularity of old blog posts drop exponentially with time? These are some of the questions that we address in this work. Our goal is to build a model that generates realistic cascades, so that it can help us with link prediction and outlier detection. Blogs (weblogs) have become an important medium of information because of their timely publication, ease of use, and wide availability. In fact, they often make
more » ... s, by discussing and discovering evidence about political events and facts. Often blogs link to one another, creating a publicly available record of how information and influence spreads through an underlying social network. Aggregating links from several blog posts creates a directed graph which we analyze to discover the patterns of information propagation in blogspace, and thereby understand the underlying social network. Not only are blogs interesting on their own merit, but our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks. Here we report some surprising findings of the blog linking and information propagation structure, after we analyzed one of the largest available datasets, with 45,000 blogs and ~ 2.2 million blog-postings. Our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks. We also present a simple model that mimics the spread of information on the blogosphere, and produces information cascades very similar to those found in real life.
arXiv:0704.2803v1 fatcat:zcjpn3s3zjf7lo57uk23psigs4

Statistical Properties of Social Networks [chapter]

Mary McGlohon, Leman Akoglu, Christos Faloutsos
2011 Social Network Data Analytics  
In this chapter we describe patterns that occur in the structure of social networks, represented as graphs. We describe two main classes of properties, static properties, or properties describing the structure of snapshots of graphs; and dynamic properties, properties describing how the structure evolves over time. These properties may be for unweighted or weighted graphs, where weights may represent multi-edges (e.g. multiple phone calls from one person to another), or edge weights (e.g.
more » ... ry amounts between a donor and a recipient in a political donation network).
doi:10.1007/978-1-4419-8462-3_2 fatcat:huvb2zqnnvhmznpghn4s56hl34

Graph Mining: Laws and Generators [chapter]

Deepayan Chakrabarti, Christos Faloutsos, Mary McGlohon
2010 Managing and Mining Graph Data  
How does the Web look? How could we tell an "abnormal" social network from a "normal" one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks, to sociology, to biology, and many more. Indeed, any : relation in database terminology can be represented as a graph. Many of these questions boil down to the following: "How can we generate synthetic but realistic graphs?" To answer this, we must first
more » ... and what patterns are common in real-world graphs, and can thus be considered a mark of normality/realism. This survey gives an overview of the incredible variety of work that has been done on these problems. One of our main contributions is the integration of points of view from physics, mathematics, sociology and computer science.
doi:10.1007/978-1-4419-6045-0_3 dblp:series/ads/ChakrabartiFM10 fatcat:e32miejxv5gpznzuwhj24wym3e

oddball: Spotting Anomalies in Weighted Graphs [chapter]

Leman Akoglu, Mary McGlohon, Christos Faloutsos
2010 Lecture Notes in Computer Science  
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem to govern the socalled "neighborhood sub-graphs" and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design OddBall, so that it is
more » ... le and it can work un-supervised (no user-defined constants) and (c) we report experiments on many real graphs with up to 1.6 million nodes, where OddBall indeed spots unusual nodes that agree with intuition.
doi:10.1007/978-3-642-13672-6_40 fatcat:xym6jah6lvbhxmvjbfkpjk2674

Dynamics of conversations

Ravi Kumar, Mohammad Mahdian, Mary McGlohon
2010 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10  
How do online conversations build? Is there a common model that is followed in human communication? In this work we explore these questions in detail. By considering three different social datasets, namely, Usenet groups, Yahoo! Groups, and Twitter, we analyze the structure of conversations in each of these datasets. We propose simple mathematical models for the generation of basic conversation structures and then refine this model to take into account the identities of each member of the conversation.
doi:10.1145/1835804.1835875 dblp:conf/kdd/KumarMM10 fatcat:gpeidauufjawfck6t3fe3t35wm

SNARE

Mary McGlohon, Stephen Bay, Markus G. Anderle, David M. Steier, Christos Faloutsos
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
Mary Mc-Glohon was partially supported by a Yahoo! Key Technical Challenges Grant.  ... 
doi:10.1145/1557019.1557155 dblp:conf/kdd/McGlohonBASF09 fatcat:zl6mwtqbevhitlcvrodc6b5mmi

Weighted graphs and disconnected components

Mary McGlohon, Leman Akoglu, Christos Faloutsos
2008 Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08  
The vast majority of earlier work has focused on graphs which are both connected (typically by ignoring all but the giant connected component), and unweighted. Here we study numerous, real, weighted graphs, and report surprising discoveries on the way in which new nodes join and form links in a social network. The motivating questions were the following: How do connected components in a graph form and change over time? What happens after new nodes join a network-how common are repeated edges?
more » ... study numerous diverse, real graphs (citation networks, networks in social media, internet traffic, and others); and make the following contributions: (a) we observe that the non-giant connected components seem to stabilize in size, (b) we observe the weights on the edges follow several power laws with surprising exponents, and (c) we propose an intuitive, generative model for graph growth that obeys observed patterns.
doi:10.1145/1401890.1401955 dblp:conf/kdd/McGlohonAF08 fatcat:3yu47sag55dazki7kixfjpiaoi

Patterns of Cascading Behavior in Large Blog Graphs [chapter]

Jure Leskovec, Mary McGlohon, Christos Faloutsos, Natalie Glance, Matthew Hurst
2007 Proceedings of the 2007 SIAM International Conference on Data Mining  
How do blogs cite and influence each other? How do such links evolve? Does the popularity of old blog posts drop exponentially with time? These are some of the questions that we address in this work. Blogs (weblogs) have become an important medium of information because of their timely publication, ease of use, and wide availability. In fact, they often make headlines, by discussing and discovering evidence about political events and facts. Often blogs link to one another, creating a publicly
more » ... ailable record of how information and influence spreads through an underlying social network. Aggregating links from several blog posts creates a directed graph which we analyze to discover the patterns of information propagation in blogspace, and thereby understand the underlying social network. Here we report some surprising findings of the blog linking and information propagation structure, after we analyzed one of the largest available datasets, with 45, 000 blogs and ≈ 2.2 million blog-postings. Our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks.
doi:10.1137/1.9781611972771.60 dblp:conf/sdm/LeskovecMFGH07 fatcat:p7p2vhmh6jc3rkk6xn5lhffily

Patterns on the Connected Components of Terabyte-Scale Graphs

U. Kang, Mary McGlohon, Leman Akoglu, Christos Faloutsos
2010 2010 IEEE International Conference on Data Mining  
How do connected components evolve? What are the regularities that govern the dynamic growth process and the static snapshot of the connected components? In this work, we study patterns in connected components of large, real-world graphs. First, we study one of the largest static Web graphs with billions of nodes and edges and analyze the regularities among the connected components using GFD(Graph Fractal Dimension) as our main tool. Second, we study several time evolving graphs and find
more » ... patterns and rules that govern the dynamics of connected components. We analyze the growth rates of top connected components and study their relation over time. We also study the probability that a newcomer absorbs to disconnected components as a function of the current portion of the disconnected components and the degree of the newcomer. Finally, we propose a generative model that explains both the dynamic growth process and the static regularities of connected components.
doi:10.1109/icdm.2010.121 dblp:conf/icdm/KangMAF10 fatcat:6s4whqtxfndufjaz44r3rvz3ty

RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs

Leman Akoglu, Mary McGlohon, Christos Faloutsos
2008 2008 Eighth IEEE International Conference on Data Mining  
How do real, weighted graphs change over time? What patterns, if any, do they obey? Earlier studies focus on unweighted graphs, and, with few exceptions, they focus on static snapshots. Here, we report patterns we discover on several real, weighted, time-evolving graphs. The reported patterns can help in detecting anomalies in natural graphs, in making link prediction and in providing more criteria for evaluation of synthetic graph generators. We further propose an intuitive and easy way to
more » ... truct weighted, time-evolving graphs. In fact, we prove that our generator will produce graphs which obey many patterns and laws observed to date. We also provide empirical evidence to support our claims.
doi:10.1109/icdm.2008.123 dblp:conf/icdm/AkogluMF08 fatcat:lpqr6yqiyzazvlyhy3yujovype

OddBall: Spotting Anomalies in Weighted Graphs

Leman Akoglu, Mary McGlohon, Christos Faloutsos
2018
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the oddball algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem to govern the so-called "neighborhood sub-graphs" and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design oddball, so that it is
more » ... ble and it can work un-supervised (no user-defined constants) and (c) we report experiments on many real graphs with up to 1.6 millionnodes, where oddball indeed spots unusual nodes that agree with intuition.
doi:10.1184/r1/6607802.v1 fatcat:zomcgai5g5fkjntns5qmz4wema

Structural Analysis of Large Networks: Observations and Applications

Mary McGlohon
2018
Network data (also referred to as relational data, social network data, real graph data) has become ubiquitous, and understanding patterns in this data has become an important research problem. We investigate how interactions in social networks are formed and how these interactions facilitate diffusion, model these behaviors, and apply these findings to real-world problems. We examined graphs of size up to 16 million nodes, across many domains from academic citation networks, to campaign
more » ... utions and actor-movie networks. We also performed several case studies in online social networks such as blogs and message board communities. Our major contributions are the following: (a) We discover several surprising patterns in network topology and interactions, such as Popularity Decay power law (in-links to a blog post decay with a power law with -1:5 exponent) and the oscillating size of connected components; (b) We propose generators such as the Butterfly generator that reproduce both established and new properties found in real networks; (c) several case studies, including a proposed method of detecting misstatements in accounting data, where using network effects gave a significant boost in detection accuracy.
doi:10.1184/r1/6723230.v1 fatcat:gcdivthfjfa4rdxpf4d3rconfy

Finding Patterns in Blog Shapes and Blog Evolution

Mary McGlohon, Jure Leskovec, Christos Faloutsos, Matthew Hurst, Natalie Glance
2018
Jure Leskovec was partially supported by a Microsoft Research Graduate Fellowship, and Mary McGlohon was partially supported by a National Science Foundation Graduate Research Fellowship.  ... 
doi:10.1184/r1/6605672 fatcat:2fibnzpm2rbwdgume36ogamxuu

Page 491 of Guernsey Breeders' Journal Vol. 134, Issue 5 [page]

1974 Guernsey Breeders' Journal  
.; 2, Clearbranch Valon Buttercup, Mary Collins, Fort Valley, Ga.; 3, Idylwoods Command Pamela, Norman McGlohon, Athens, Ga.  ...  JUNIOR YEARLING HEIFER 1, Gearbranch Romans Del^t, Mary CoUiiu; 2, Myrtledales Falcon Tulip, Nor¬ man McGlohon; 3, Gearbranch Predict Pandora, H. J. Haga, III, Fort Valley, Ga.  ... 

Page 502 of Guernsey Breeders' Journal Vol. 132, Issue 5 [page]

1973 Guernsey Breeders' Journal  
FITTING AND SHOWING 1, Norman McGlohon; 2, NeB McGlohon; 3, Laura Peten.  ...  JUNIOR YEARLING HEIFER 1, Lodeshore Reward Tamie, Tamalyn Fish; 2, Lodeshore P D Sproce, Jiiiuny Fish, Hkk(»y Comers; 3, Cedar Manor Hornets Marie, Donald Alknna, lUckory Comers.  ... 
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