Ken Wakita

Abstracting Object Interactions Using Composition Filters

It is generally claimed that object-based models are very suitable for building distributed system architectures since object interactions follow the client-server model. To cope with the complexity of todays distributed systems, however, we think that high-level linguistic mechanisms are needed to effectively structure, abstract and reuse object interactions. For example, the conventional object-oriented model does not provide high-level language mechanisms to model layered system architectures. Moreover, we consider the message passing model of the conventional object-oriented model as being too low-level because it can only specify object interactions that involve two partner objects at a time and its semantics cannot be extended easily. This paper introduces Abstract Communication Types (ACTs), which are objects that abstract interactions among objects. ACTs make it easier to model layered communication architectures, to enforce the invariant behavior among objects, to reduce the complexity of programs by hiding the interaction details in separate modules and to improve reusability through the application of object-oriented principles to ACT classes. We illustrate the concept of ACTs using the composition filters model.

Finding community structure in mega-scale social networks: [extended abstract]

Community analysis algorithm proposed by Clauset, Newman, and Moore (CNM algorithm) finds community structure in social networks. Unfortunately, CNM algorithm does not scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. We show that this inefficiency is caused from merging communities in unbalanced manner and that a simple heuristics that attempts to merge community structures in a balanced manner can dramatically improve community structure analysis. The proposed techniques are tested using data sets obtained from existing social networking service that hosts 5.5 million users. We have tested three three variations of the heuristics. The fastest method processes a SNS friendship network with 1 million users in 5 minutes (70 times faster than CNM) and another friendship network with 4 million users in 35 minutes, respectively. Another one processes a network with 500,000 nodes in 50 minutes (7 times faster than CNM), finds community structures that has improved modularity, and scales to a network with 5.5 million.

SmartColor: disambiguation framework for the colorblind

Failure in visual communication between the author and the colorblind reader is caused when color effects that the author expects for the reader to experience are not observed by the reader. The proposed framework allows the author to annotate his/her intended color effects to the colored document. They are used to generate a repainted document that let the colorblind enjoy similar color effects that normal color vision person does for the original document. The annotations are formulated as a set of mathematical constraints that can describe several commonly used color effects. Constraints are defined over the normal vision color space. Then they are projected onto the restricted color space that corresponds to the one that the colorblind perceives. Finally, the projected constraints are resolved for the search of best repainting of the document that most successfully presents to the colorblind person the color effects experienced by the normal vision person on the original document. Effectiveness of the proposal is shown by colorblind simulation.

Extracting Multi-facet Community Structure from Bipartite Networks

Bipartite networks can represent various kinds of structures, dynamics, and interaction patterns found in social activities. M. E. J. Newman proposed a measure by which you can quantitatively evaluate the quality of network division, but his work is only applicable to uniform networks. This article extends his work and proposes a new modularity measure that can be applied to bipartite networks as well. Unlike the biparitite modularity measures previously proposed, the new measure acknowledges the fact that each individual in the society has more than just one aspect, and can thus be used to extract multi-faceted community structures from bipartite networks. The mathematical properties of the proposal is examined and compared with previous work. Empirical evaluation is conducted by using a data set synthesized from an artificial model and a real-life data set found in the field of ethnography.

A FRAMEWORK FOR COMMUNITY DETECTION IN HETEROGENEOUS MULTI-RELATIONAL NETWORKS

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.

Detecting network communities beyond assortativity-related attributes.

In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educational level, religious belief, and language. Thus, various attributes jointly affect the network topology. An interesting problem is to detect community structure beyond some specific assortativity-related attributes ρ, i.e., to take out the effect of ρ on network topology and reveal the hidden community structures which are due to other attributes. An approach to this problem is to redefine the null model of the modularity measure, so as to simulate the effect of ρ on network topology. However, a challenge is that we do not know to what extent the network topology is affected by ρ and by other attributes. In this paper, we propose a distance modularity, which allows us to freely choose any suitable function to simulate the effect of ρ. Such freedom can help us probe the effect of ρ and detect the hidden communities which are due to other attributes. We test the effectiveness of distance modularity on synthetic benchmarks and two real-world networks.

Interactive high-dimensional visualization of social graphs

The paper tackles the problems of the “giant hairballs”, the dense and tangled structures often resulting from visualization of large social graphs. Proposed is a high-dimensional rotation technique called AGI3D, combined with an ability to filter elements based on social centrality values. AGI3D is targeted for a high-dimensional embedding of a social graph and its projection onto 3D space. It allows the user to rotate the social graph layout in the high-dimensional space by mouse dragging of a vertex. Its high-dimensional rotation effects give the user an illusion that he/she is destructively reshaping the social graph layout but in reality, it assists the user to find a preferred positioning and direction in the high-dimensional space to look at the internal structure of the social graph layout, keeping it unmodified. A prototype implementation of the proposal called Social Viewpoint Finder is tested with about 70 social graphs and this paper reports four of the analysis results.

First Class Messages as First Class Continuations

First class messages, which we call message continuations, provide object-oriented concurrent programming languages with extensibility in modeling and programming communication schemes such as asynchronous communication, multicasting, sophisticated synchronization constraints, inter-object synchronization, concurrency control, resource management, and so on. In spite of its powerful extensibility, the framework is sound in that the framework guarantees that no program can destroy the semantics of the built-in communication primitives. This good property was obtained by categorization of message continuations and careful design of the primitive operations on message continuations.

Community Detection in Multi-Partite Multi-Relational Networks Based on Information Compression

Community detection in uni-partite single-relational networks which contain only one type of nodes and edges has been extensively studied in the past decade. However, many real-world systems are naturally described as multi-partite multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose an information compression based method for detecting communities in such networks. Specifically, based on the minimum description length (MDL) principle, we propose a quality function for evaluating partitions of a multi-partite multi-relational network into communities, and develop a heuristic algorithm for optimizing the quality function. We demonstrate that our method outperforms the state-of-the-art techniques in both synthetic and real-world networks.

An implementation of a hygienic syntactic macro system for JavaScript: a preliminary report

The article describes an implementation scheme of a hygienic syntactic macro system for JavaScript. Instead of implementing the complex logic of a hygienic macro system from scratch, the proposed method heavily relies on an existing Scheme implementation of its hygienic syntactic macro system. A program written in our macro-enhanced version of JavaScript is first translated into a Scheme program. It is then macro-expanded by a macro expander of Scheme into a macro-free Scheme code. Finally, it is translated back to Javascript, which at this point is free of macros. To deal with the macro-enhanced syntax, an extensible parser architecture based on top-down operator precedence is proposed. A prototype hygienic macro system, including both the parser and the two-way translator, is implemented by only 2,000 lines of Scheme code.