12 research outputs found
Multi-dimensional Conversation Analysis across Online Social Networks
With the advance of the Internet, ordinary users have created multiple
personal accounts on online social networks, and interactions among these
social network users have recently been tagged with location information. In
this work, we observe user interactions across two popular online social
networks, Facebook and Twitter, and analyze which factors lead to retweet/like
interactions for tweets/posts. In addition to the named entities, lexical
errors and expressed sentiments in these data items, we also consider the
impact of shared user locations on user interactions. In particular, we show
that geolocations of users can greatly affect which social network post/tweet
will be liked/ retweeted. We believe that the results of our analysis can help
researchers to understand which social network content will have better
visibility.Comment: Datasets will be anonymized and published at:
http://akcora.wordpress.com/2013/12/24/pointer-for-datasets
Risks of Friendships on Social Networks
In this paper, we explore the risks of friends in social networks caused by
their friendship patterns, by using real life social network data and starting
from a previously defined risk model. Particularly, we observe that risks of
friendships can be mined by analyzing users' attitude towards friends of
friends. This allows us to give new insights into friendship and risk dynamics
on social networks.Comment: 10 pages, 8 figures, 3 tables. To Appear in the 2012 IEEE
International Conference on Data Mining (ICDM
Blockchain: A Graph Primer
Bitcoin and its underlying technology Blockchain have become popular in
recent years. Designed to facilitate a secure distributed platform without
central authorities, Blockchain is heralded as a paradigm that will be as
powerful as Big Data, Cloud Computing and Machine learning. Blockchain
incorporates novel ideas from various fields such as public key encryption and
distributed systems. As such, a reader often comes across resources that
explain the Blockchain technology from a certain perspective only, leaving the
reader with more questions than before. We will offer a holistic view on
Blockchain. Starting with a brief history, we will give the building blocks of
Blockchain, and explain their interactions. As graph mining has become a major
part its analysis, we will elaborate on graph theoretical aspects of the
Blockchain technology. We also devote a section to the future of Blockchain and
explain how extensions like Smart Contracts and De-centralized Autonomous
Organizations will function. Without assuming any reader expertise, our aim is
to provide a concise but complete description of the Blockchain technology.Comment: 16 pages, 8 figure
Data depth and core-based trend detection on blockchain transaction networks
Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC–while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT
Topological data analysis (TDA) delivers invaluable and complementary
information on the intrinsic properties of data inaccessible to conventional
methods. However, high computational costs remain the primary roadblock
hindering the successful application of TDA in real-world studies, particularly
with machine learning on large complex networks.
Indeed, most modern networks such as citation, blockchain, and online social
networks often have hundreds of thousands of vertices, making the application
of existing TDA methods infeasible. We develop two new, remarkably simple but
effective algorithms to compute the exact persistence diagrams of large graphs
to address this major TDA limitation. First, we prove that -core of a
graph suffices to compute its persistence diagram,
. Second, we introduce a pruning algorithm for graphs to
compute their persistence diagrams by removing the dominated vertices. Our
experiments on large networks show that our novel approach can achieve
computational gains up to 95%.
The developed framework provides the first bridge between the graph theory
and TDA, with applications in machine learning of large complex networks. Our
implementation is available at
https://github.com/cakcora/PersistentHomologyWithCoralPrunitComment: Spotlight paper at NeurIPS 202