Moderator: Ian Wood
Moderator: Nathaniel Rodriguez
Moderator: Sol Lim
Paper: Two’s company, three (or more) is a simplex Algebraic-topological tools for understanding higher-order structure in neural data
Paper: Towards an integration of deep learning and neuroscience (Part 1)
Justification: Deep learning and neuroscience has developed a lot since their departure. It is time to look back together and see what can be learned from each other. The paper is also very popular in votes. Since this is such a long paper with a rich literature behind it, I intend to cover only the first 3 sections. The later half, especially section 4, involves more detailed brain functionalities that I am not qualified to take on.
The physics of spreading processes in multilayer networks
Manlio De Domenico, Clara Granell, Mason A Porter & Alex Arenas
Bennett CM, Baird AA, Miller MB, Wolford GL 15th Annual Meeting of the Organization for Human Brain Mapping. San Francisco, CA; 2009. Neural correlates of interspecies perspective taking in the post-mortem atlantic salmon: an argument for proper multiple comparisons correction.
Errata for Cluster failure: http://blogs.warwick.ac.uk/nichols/entry/errata_for_cluster/
Justification: Multiple comparisons correction, in particular, in neuroscience is still dangerously neglected, although it can be catastrophically detrimental to our understanding of the brain (or any kind of findings for that matter). The dead salmon study made a great satirical argument to show false positive activity from a dead salmon! The cluster failure study deals with more field-specific issues but many people got interested in the study because it was covered by many major media with some over-excitement(?).
Date : July 15, 2016
Moderator : Xiaoran Yan
Date : April 29, 2016
Moderator : Pikmai Hui
Paper: Global Diffusion via Cascading Invitations: Structure, Growth, and Homophily
Justification: This paper analyzes the effect of homophily in global diffusion. I am curious to learn more about their approach!
Moderator: Ian Wood
Paper: Time Complexity of Evolutionary Algorithms for Combinatorial Optimization
Justification: Evolutionary algorithms are a heuristic search method for exploring complex parameter spaces. They are introduced early in the complex systems program, and are a good example of a bio-inspired method for machine learning. However, it is unclear how good the fit or efficient the search might be for a particular problem. This paper describes some of the methods to apply bounds to describe the run-time complexity of such algorithms applied to a number of different problems, along with some discussion of when to use variations.
Date: April 8, 2016
Justification: Human vs Machine, with YY as the moderator who knows both Go and deep learning. What else do you need?
Date: April 1, 2016
Justification: The alphago game really got me interested in the theory of deep learning. This should be helpful to prepare for the alphago paper when YY present it the week after the next. Many of you know a lot more about deep learning than I do, I look forward to your opinions.
Date: March, 11, 2016
Moderator: Pikmai Hui
Justification: Recently we have acquired access to the Web-of-Science dataset. While Rion is working on structuring the data, I think it may be of our interests to think of ways to keep track of series of partitions/communities. Many studies focuses on unique snapshot of a network, and assume implicitly that the network remain static within the period it is collected. With datasets that span a long time period, such as the Web-of-Science, it is possible to construct ordered sequence of snapshots and study the evolution of communities across snapshots. This paper describes one way of doing it.
Date: March 4, 2016
Moderator: Yizhi Jing
Paper Title: Causal Entropic Forces
Justification: One of the highly voted papers. This paper brings together interesting topics in information theory, causality, intellegence and evolution by including them into a thermodynamic model. Many of us are familiar with some of these topics, but it may be interesting to look at them from a novel perspective.
Date: Feb 26, 2016
Moderator: Ian Wood
Paper Title: Latent Dirichlet Allocation
Justification: This paper presents the original Latent Dirichlet Allocation model. This is an important method for topic modeling, and the paper is also a good example of Bayesian modeling. I have read papers using and extending the model, but I think many of us have not read the original paper, so I think it would be informative to step back and discuss this paper.
Date: Feb 12, 2016
Moderator: Jaehyuk Park
Paper Title: Scaling and universality in urban economic diversification
Justification: This paper presents a simple mathematical derivation of the universality, and provide a model, together with its economic implications of open-ended diversity created by urbanization, for understanding the observed empirical distribution. I chose it because 1) it would be helpful to many of our members, who are interested in urban economic mobility & development, and 2) this seems also good to be linked with researches done by Rossano (who visited our school in the last week), to extend our discussion.
Date: Feb 5, 2016
Moderator: Azadeh Nemtzadeh
Paper Title: Determinants of Meme Popularity
Justification: This paper introduces a generative model for information spreading in online social media, focusing on two factors affecting meme popularity. These two factors are the structure of social network and the memory time of users.
Date: Nov 29, 2016
Moderator: Xiaoran Yan
Paper Title: Structure and inference in annotated networks
Justification: This paper generalized stochastic block model by incorporate meta data on nodes. Using a Bayesian framework, the community detection algorithm is able to leverage the annotations if they are correlated with community structures, or ignore them when they are independent.
Date: Nov 22, 2015
Moderator: Krishna Bathina
Paper Title: Principles of assembly reveal a periodic table of protein complexes
Justification: This paper examines patterns in the quaternary structure of human proteins by looking at three independent sets of data. These patterns are organized into a ‘periodic table’ and are used to predict currently undiscovered structures. I chose this paper because it is a good example of analyzing, predicting, and validation on a data set.
Date: Nov 20, 2015
Paper Title: Secondhand Brokerage: Evidence on the Importance of Local Structure for Managers, Bankers,and Analysts.
Justification: This paper examines under what specific topological situation the structural hole position can have advantages throughout the dataset of bankers. I chose this because it can provide us a sociological approach to network analysis that rarely be considered in our field.
Date: Nov 6, 2015 Moderator: Azadeh
Paper Title: Finding Statistically Significant Communities in Networks
Justification: We had read several papers related to network community. I find this paper pretty relevant to some of the projects that the reading group attendees are involved with. In this paper, authors proposed a method to detect clusters in networks accounting for edge directions, edge weights, overlapping communities.
Paper title: Quantifying Creativity in Art Networks
Justification: Interesting article on computational creativity. Their method of quantifying creativity via a creativity implication network could be applied towards any cultural product.
Date: Oct 23, 2015
Paper Title: Memory in network flows and its effects on spreading dynamics and community detection
Justification: One of the most voted articles. This paper argues for the importance of taking memory into account in modeling spreading process and doing community detection. This was made by studying the effects of second-order Markov dynamics in real-world networks.
Date: Oct 16, 2015
Paper Title: Influence maximization in complex networks through optimal percolation
Justification: Most voted article. The authors proposed a method that maps the problem of identifying influencers onto optimal percolation, and found that a large number of optimal influencers are low-degree nodes.
Paper title: word2vec
Justification: word2vec is a very cool ‘deep-learning’ technique to learn dense vectors that represent words, from a large set of documents. It allows analogical inferences (e.g. v(‘king’) – v(‘man’) + v(‘woman’) ~ v(‘queen’) and people demonstrated that the word vectors and document vectors can improve various text mining tasks (e.g. sentiment analysis) significantly.
Date: September 25, 2015
Paper Title: Topological data analysis of contagion maps for examining spreading process in networks
Justification: Modern networks, digital or physical, often have “long-range” edges, which connects nodes that are otherwise far away from each other. They can be flights from Asia to America, or likings of a video from a different culture. We all know this is called the small-world effect. This paper considers this effect as noises to a classical concept of geometric contagion. Yes, it builds new models. More importantly, it shares a new angle from which we can construct a low-dimensional structure of networks. This is quite useful in our research in general.
Date: September 18, 2015
Moderator : Ian
Paper Title: Mapping Change in Large Networks
Date: September 11, 2015
Paper Title: Symbolic regression of generative network models
Justification: In the past we have covered a couple different network generating models. In continuing that theme I wanted to try a paper that takes a rather different approach toward generating networks. In previous papers we saw hyperbolic geometry and the block model used in combination with bayesian methods to find generative models for networks. This paper uses evolution to train a symbolic representation of the network. We can go over what advantages and disadvantages that this approach has relative to the other methods.