186 citations · 572 across the 23 of their papers we have counts for
11 papers · 1 filter
Heterogeneous Interaction Network Analysis (HINA): A New Learning Analytics Approach for Modelling, Analyzing, and Visualizing Complex Interactions in Learning Processes
Shihui Feng, Baiyue He, Dragan Gasevic +1
Existing learning analytics approaches, which often model learning processes as sequences of learner actions or homogeneous relationships, are limited in capturing the distributed,…
Belief propagation for finite networks using a symmetry-breaking source node
Seongmin Kim, Alec Kirkley
Belief Propagation (BP) is an efficient message-passing algorithm widely used for inference in graphical models and for solving various problems in statistical physics. However, BP…
Mutual information and the encoding of contingency tables
Maximilian Jerdee, Alec Kirkley, M. E. J. Newman
Mutual information is commonly used as a measure of similarity between competing labelings of a given set of objects, for example to quantify performance in classification and comm…
Inference of dynamic hypergraph representations in temporal interaction data
Alec Kirkley
A range of systems across the social and natural sciences generate datasets consisting of interactions between two distinct categories of items at various instances in time. Online…
Normalized mutual information is a biased measure for classification and community detection
Maximilian Jerdee, Alec Kirkley, M. E. J. Newman
Normalized mutual information is widely used as a similarity measure for evaluating the performance of clustering and classification algorithms. In this paper, we argue that result…
Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection
Tiago P. Peixoto, Alec Kirkley
The task of community detection, which aims to partition a network into clusters of nodes to summarize its large-scale structure, has spawned the development of many competing algo…
Clustering of heterogeneous populations of networks
Jean-Gabriel Young, Alec Kirkley, M. E. J. Newman
Statistical methods for reconstructing networks from repeated measurements typically assume that all measurements are generated from the same underlying network structure. This nee…
Representative community divisions of networks
Alec Kirkley, M. E. J. Newman
Methods for detecting community structure in networks typically aim to identify a single best partition of network nodes into communities, often by optimizing some objective functi…
The friendship paradox in real and model networks
George T. Cantwell, Alec Kirkley, M. E. J. Newman
The friendship paradox is the observation that the degrees of the neighbors of a node in any network will, on average, be greater than the degree of the node itself. In common parl…
Mixing Patterns in Interdisciplinary Collaboration Networks: Assessing Interdisciplinarity Through Multiple Lenses
Shihui Feng, Alec Kirkley
There are inherent challenges to interdisciplinary research collaboration, such as bridging cognitive gaps and balancing transaction costs with collaborative benefits. This raises…
Balance in signed networks
Alec Kirkley, George T. Cantwell, M. E. J. Newman
We consider signed networks in which connections or edges can be either positive (friendship, trust, alliance) or negative (dislike, distrust, conflict). Early literature in graph…