Abstract

The study of complex networks or graphs has been extensively pursued by researchers from multiple disciplines. In history, the well-known “Seven Bridges of Koenigsberg” problem is the first real-world problem that was solved by the study of networks. Since then, there has been significant theoretical advancement in this area. Network science has been explored in diverse fields such as sociology, biology, physics and computer science and has spanned a plethora of applications like link analysis, community detection, influence analysis. Only recently, detecting outliers in such information networks has caught the attention of researchers in the data mining community. Outlier detection in information networks is the focus of this thesis. Outliers are structures in networks which have unusual patterns, which deviate significantly from the rest of the data. Outliers in graphs can appear in different forms like nodes, edges, subgraphs, communities and so on. In this thesis, we propose a novel outlier structure in networks, graph cuboid outliers. We put forward the novel problem of graph cuboid outlier detection in both static and timeevolving networks and design efficient algorithms for the same. Recently, there has been an interest in finding outliers with respect to a given query. Query-based outlier detection is more favorable over the general outliers as it allows the user a flexibility to find outliers following a particular schema and predicates encoded in the form of a query. Given a heterogeneous network and a query we detect Graph Cuboid Outliers (GCOutliers). Graph cuboids are semantically related regions in a network which helps us view the network along multiple dimensions and at multiple levels. GCOutliers are those cuboids which exhibit anomalous behavior with respect to the given subgraph query. An example of a GCOutlier would be those research areas which have unusual collaborations in a DBLP network. Further, we can obtain such regions with respect to specific requirements, such as finding those research areas where a large numbers of papers are published with at least five authors. The requirement is modeled as a subgraph query to obtain query sensitive graph cuboid outliers. In order to detect GCOutliers, there are several challenges; (i) the number of cuboids can be high, (ii) the number of matches can be large and (iii) subgraph isomorphism is an NP-hard problem. In our solution we address the issue of the large number of matches. This is done by designing a na¨ıve random sampling method followed by a more principled sampling method using regression models for nodes and edges. We next move our attention to time-evolving or temporal networks. For a series of snapshots of a heterogeneous temporal network we propose to detect Evolutionary Graph Cuboid Outliers (EGCOutliers) in temporal networks. EGCOutliers are those cuboids which have an anomalous behaviour with respect to the cuboids in its own snapshot and whose behavior over time is different from the average behavior of all the cuboids over time. The change in behavior over time is considered as the evolutionary behavior and therefore, we call our graph cuboid outliers as Evolutionary Graph Cuboid Outliers. We extend GCOutlier detection to detect EGCOutliers. We model the problem of detecting EGCOutliers into a regression problem. The experimental results on both real and synthetic datasets show the efficiency of the proposed algorithms. We posit the problem of detecting GCOutliers and EGCOutliers in heterogeneous networks and proposed efficient solutions for the same. We believe this thesis laid the foundation for graph cuboid outliers and opens up a new direction in the area of outlier detection research. As network based systems continue to grow and become ubiquitous, we believe that the proposed algorithms will receive attention in a wide variety of applications and will foster the area of outlier detection in general.

Abstract

Recent trends in natural language processing (NLP) show ever increasing popularity of dependencybased analysis of natural language texts. Dependency representations offer simplicity, compactness and transparent encoding of predicate-argument structure. In the last decade and a half, a number of dependency treebanks have been built and various parsing algorithms have been proposed for automatic dependency analysis across wide range of languages. Over the years, it has been observed that morphologically rich and free word order languages, unlike fixed word order languages, are harder to parse, regardless of the parsing technique used. On the one hand, rich morphology provides explicit cues for parsing, while on the other hand it worsens the problem of data sparsity as it leads to high lexical diversity and variation in word order. In this thesis, we aim to address this trade-off for accurate and robust parsing of morphologically rich Indian languages. We present novel strategies to effectively represent morphology in the parsing models and also to mitigate the effect of its trade-offs. We propose to represent morphosyntactic information as higher-order features under the Markovian assumption. More specifically, we use the history of a transition-based parser to extract and propagate morphological information such as case and grammatical agreement as higher-order features for parsing nominal nodes. Despite its benefits, rich morphology can also pose a multitude of challenges to statistical parsing. The most prominent issue is related to sampling bias towards canonical structures of a language. As current parsers are mostly trained on formal texts, even a slight deviation from canonical word order can severely affect their performance. To overcome this bias, we propose a sampling technique to generate training instances with diverse word orders from the available canonical structures. We show that linearly interpolated models trained on diverse views of the same data can effectively parse both canonical and non-canonical texts. Similarly, to mitigate the effect of lexical sparsity, we use supervised domain adaptation techniques for training parsers on lexically more diverse annotations from augmented Hindi and Urdu treebanks. We demonstrate that a feedforward neural network-based dependency parser trained on augmented, harmonized Hindi and Urdu data performs significantly better than the parsing models trained separately on their individual datasets. Furthermore, we explore lexical semantics as a viable alternative to more training data for parsing semantically rich but sparse dependency annotations in Indian language treebanks. We show that lexical semantics in the form of discrete and continuous features such as ontological categories, Brown clusters and word embeddings can play a major role in disambiguating highly rich dependency relations.

Abstract

Improving the thermostability of industrial enzymes is an important protein engineering challenge. Molecular level understanding of mutational effects on stability and activity of enzymes is complex particularly when several point mutations are incorporated during the directed evolution experiments, due to non-additivity involving either cooperative (positive) or antagonistic (negative) effects. Thermostability is experimentally characterized by many observables (T50, Tm, T1/2, ΔG etc.) and they often show correlation with each other. However, this is not always true because of the different stabilizing mechanisms operative in different cases. In this thesis, I have tried to understand the molecular mechanism of thermostability and activity of B. subtilis lipase mutants. I have studied the molecular dynamics of wild type B. subtilis lipase (WT) and its six progressively thermostable mutants (2M, 3M, 4M, 6M, 9M, and 12M), at different temperatures. The less thermostable mutants (LTMs), 2M to 6M, show WT-like dynamics at all simulation temperatures. However, the two more thermostable mutants 9M and 12M (MTMs) show the required flexibility at appropriate temperature ranges and maintain conformational stability even at high temperature. They show a deep and rugged free-energy landscape, confining them within a near-native conformational space by conserving non-covalent interactions, and thus protecting them from possible aggregation. In contrast, the LTMs having marginally higher thermostabilities than WT show greater probabilities of accessing non-native conformations, which, due to aggregation, have reduced possibilities of reverting to their respective native states under refolding conditions. Our analysis indicates the possibility of non-additive effects of point mutations on the conformational stability of LTMs. I suggested the lack of consistency in the effect of point mutations incorporated during the initial generations of directed evolution experiments, towards conformational stabilization of B. subtilis lipase mutants of later generations. I show that the cumulative point mutations incorporated in mutants 2M (with two point mutations) to 6M (with six point mutations) possibly do not retain their original stabilizing nature in the most thermostable 12M mutant (with 12 point mutations). I have carried out MD simulations using structures incorporating reversal of different sets of point mutations to assess their effect on the conformational stability and activity of 12M. My analysis has revealed that reversal of certain point mutations in 12M had little effect on its conformational stability, suggesting that these mutations were probably inconsequential towards the thermostability of the 12M mutant. Interestingly, these mutations involved evolutionarily conserved residues. On the other hand, some of the other point mutations incorporated in non-conserved regions, appeared to contribute significantly towards the conformational stability and/or activity of 12M. Based on the analysis of dynamics of in-silico mutants generated using the consensus sequence, I identified experimentally verifiable residue positions to further increase the conformational stability and activity of the 12M mutant. In this context, I also studied, the protein contact networks (PCNs) and its application in understanding the small conformational variations due to point mutations using both, the crystal structures as well as the MD trajectories of the different mutants. Analysis of the variation in several network centrality measures showed some important correlations with stabilities of different mutants. I have identified key residues, in 9M and 12M, which can be related to their enhanced stability and activity compared to WT and other less thermostable mutants. I have observed that the reversal of few key point mutations in 12M have significant effect on the topological features, suggesting the importance of these point mutations in the stability/activity of 12M. The study of contact networks based on non-covalent interactions effectively bring out the factors responsible for enhanced stability/activity due to point mutations in more thermostable 9M and 12M mutants. Our study highlights the importance of considering not only the geometrical criteria but also the chemistry of interacting amino acids in PCN construction. Analysis of the molecular basis of chain length selectivity in B. subtilis lipase and the role of catalytically important point mutations in the substrate binding of the 12M mutant suggest that the combined reversal of M134E and M137P point mutations cause significant decrease in binding affinity for the small chain length ester substrates compared to long chain length ester substrates, suggesting the importance of M134E and M137P point mutations in substrate binding and hence activity. The 12M mutant maintains a nearly constant volume for binding site with all the substrates and stabilizes a ready to attack co

Abstract

In Natural Language, two senses of reference are generally described. First is the symbolic relationship that expressions (words or phrases) have with the concrete or abstract real world objects and second sense is the relationship between two textual expressions in the text, in which one expression provides the necessary information to interpret or relate the other. These expressions are distributed across the text and their connections make co-referential chains (or links). For a text, coreference resolution is a task to identify and link expressions that refer to same object. These coreference expressions can be anaphors, nominal, verbal or verb-nominal expressions. Coreference links (chains) are useful in various NLP applications such as question answering, text summarization, Machine translation, etc. For English and several other European languages, the task of resolving coreference has been studied to a sufficiently great extent and various techniques have been proposed for those languages, but for Indian Languages, work has been very limited. In this thesis, we describe our work on second sense of reference by proposing co-reference representation scheme and different co-reference relation types between continuous mentions of the same coreference chain such, as identity, near-identity and weak identity relations and their sub-types. Then, we also propose and describe methods to resolve coreference for Hindi news and dialogue text. The six major points (contributions) are covered in this thesis on the topic of coreference. First, we identified Indian language specific conceptual, structural and representational issues in the existing coreference annotation schemes and tried to resolve them by proposing a unified coreference annotation framework and procedure. This framework includes various aspects of coreference like expression span, coreference chain, relation between contiguous expressions of same coreference chain, etc. Second, Based on the proposed annotation scheme, we developed a semi-automatic annotation tool (CAT - Coreference Annotation Tool) to ease the annotation process. Third, we propose a method for calculating inter annotator agreement on various aspect/level of coreference annotation. Fourth, Using proposed coreference annotation scheme and CAT, we annotated coreference information on some part of Hindi and Urdu Dependency Treebanks. Fifth, We also present a hybrid multi-classifier based approach to identify reference type for an anaphor (pronoun). We describe a hybrid (learning and rule) approach to resolve entity and event referring pronouns for Hindi dialogue and news text. In these approaches, we explore the use of dependency structures as a source of syntactic information and for entity anaphora resolution. We compare use of dependency structure based rules (features) over syntax based rules (features) for event anaphora resolution. Other than dependency based features we also explore the use of other linguistic information such as sub-topic boundary, animacy and Named Entity categories for dialogue anaphora resolution. Sixth, we present a sieve based approach for Hindi nominal reference resolution and relation type identification between continuous expressions of same coreference chains. In the this approach, we explore the use of Paninian dependency grammar, various linguistic rules based on gender, number, person, animacy, dbpedia based dictionaries, word-embeddings from word2vec - Glove as features and rules to resolve nominal co-reference. Hybrid system on these rules and features with various sieves (on predefined preference) gives considerable amount of accuracy for nominal reference resolution and relation type identification. At conclusion, we combine all the above mentioned modules (entity anaphora resolution, event anaphora resolution and nominal reference resolution) into one software-kit so the NLP community can use it and provide us feedback on the presented approaches.

Abstract

For privacy reasons, personally identifiable information like age and gender of people is not available publicly. However accurate prediction of such information has important applications in the fields of advertising, forensics and business intelligence. For instance, from a forensic linguistics perspective, being able to determine the linguistic profile of the author of a suspicious text solely by analyzing the text could be extremely valuable for evaluating suspects. Similarly, from a marketing viewpoint, companies may be interested in knowing, on the basis of the analysis of blogs and on-line product reviews, what types of people like or dislike their products. In this thesis we devise methods to predict age, gender and profession of authors from social media. Firstly we consider the problem of predicting age and gender of blog authors. We tried to exploit differences in writing style and content between male and female blogger’s as well as among authors of different ages to determine an unknown authors age and gender. N-gram words have been used as content based features and POS n-grams have been used as style based features. N-gram based features model top words used by different categories of people. But many times same words are used in different contexts. For example, males use dresses in context with trousers and coats whereas females use dresses with words like bridal wears and gowns etc. To capture these, we also use topic based features. Topic based features consider the fact that different categories of people have different topic of interests. We tried to model these differences to predict age and gender of the person. We used LDA [9] algorithm to find topics from the blog and propose a method which uses a combination of above mentioned features to predict unknown author’s age and gender by solely analyzing contents of the blogs. Existing methods for predicting age and gender of blog authors have focused on classifier learning using content based features like word n-grams and style based features like Part of Speech (POS) ngrams. Two major drawbacks of previous approaches are: (1) they do not consider the semantic relation between words, and (2) they do not handle polysemy. In this thesis, we propose a novel method to address these drawbacks by representing the document usingWikipedia concepts and category information. Experimental results show that classifiers learned using such features help us achieve significantly better accuracy compared to the state-of-the-art methods. Indeed, feature selection shows that our novel features are more effective than previously used content based features. In recent years, the microblogging services like Twitter has become a major tool for sharing events, expressing opinions and communicating with friends. Several thousand of microblogs are posted each second describing the ongoing events around the world. Twitter has large number of users with varied age, gender and professions. Because of this recent rise in the popularity and size of social media, there is a growing need for system that can extract useful users information from social media. People in advertising and marketing, try to find certain characteristics of users to target. Thus, finding characteristics (attributes) of Twitter users is of high importance. Unlike other social networking sites like Facebook and MySpace, Twitter has limited information about its users, making the task difficult. In response, this work sets out to predict automatically age, gender and profession of Twitter users. Existing methods for this problem have focused on classifier learning using the features related to user such as Socio- Linguistic, Content based and Network structure based. Most of the current systems did not consider the effect of these attributes on how other users interact with a user. In this paper, we try to exploit mention tweets and list membership information of Twitter users to predict age, gender and profession along with the tweet content. Our experiments demonstrate a high overall accuracy in detecting age, gender and profession as well as analysing the pros and cons of various types of features for particular attribute of user.

Abstract

Fuzzy processors are commonly used in different control systems. They make use of fuzzy logic while conventional processors rely on classical crisp logic. Classical logic needs accurate equations and precise data to function properly. On the other hand, fuzzy logic is a precise logic that has been created to capture the approximate, inexact nature of the real world. Fuzzy logic is a form of many-valued logic and holds truth values between 0 and 1. It aims to represent the model of human reasoning that is needed to make decisions in an environment where information is partially true, incomplete, conflicting and imprecise. Fuzzy processors are recommended for complex and non-linear processes which are difficult for conventional processors to control due to the lack of simple mathematical models. This thesis presents the ASIC design of a digital fuzzy processor using UMC 0.18 um Logic GII process. The proposed processor can work on arbitrary membership functions, resulting in higher accuracy as compared to triangular or trapezoidal functions. Separate storage of the sets of antecedents common to groups of fuzzy rules, leads to reduction in computation time. Pipelining and parallelism in the architecture speed up the computation. The architecture has three pipe stages and can operate on eight inputs in parallel. The processor works at a clock frequency of 100 MHz. For a system of 256 active rules, the processor produces results at an interval of 4.5 us and consumes 85 mW from a 1.8 V supply. The ASIC has lesser computation time and consumes lower power compared to state of the art RISC and CISC architecture processors.

Abstract

Transition-based techniques were originally introduced for syntactic parsing. They have achieved the highest accuracies for both constituency and dependency parsing. In an earlier work, transitionbased technique was extended to the task of syntactic linearization. In this work, we improve accuracies for transition-based syntactic linearization and introduce transition-based technique for deep input linearization. We thus show the effectiveness of transition-based technique for linearization. Syntactic linearization is task of producing a linearized sentence along with its dependency tree given an input of bag of words and optional set of POS and dependency constraints. The task is important for text-to-text applications such as machine translation, summarization, dialog generation etc. We identify feature sparsity issue with state-of-the-art transition-based models for syntactic linearization. We propose a modification to the standard transition-based feature structure, which reduces feature sparsity and allows lookahead features at a small cost to decoding efficiency. Our model gives the best reported accuracies on all benchmarks, yet still being over 30 times faster compared with traditional best-first-search. Deep input linearization produces linearized sentence given an input of deep graph of set of lemmas. The deep input type is intended to be an abstract representation of the meaning of a sentence. Unlike semantic input, where the nodes are semantic representations of input, deep input is more surface centric, with lemmas for each word being connected by semantic labels. In contrast to shallow syntactic trees, function words in surface forms are not included in deep graphs. Deep inputs can more commonly occur as input of Natural Language Generation (NLG) systems where entities and content words are available, and one has to generate a grammatical sentence using them with only provision for inflections of words and introduction of function words. Such usecases include summarization, dialog generation etc. Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms. Though easier to visualize, pipeline approaches suffer from error propagation. In addition, information available across modules cannot be leveraged by all modules. We construct a transition-based model to jointly perform linearization, function word prediction and morphological generation, which considerably improves upon the accuracy compared to a pipelined baseline system. On a standard deep input linearization shared task, our system achieves the best results reported so far.

Abstract

Neuroimaging methods like resting state fMRI and diffusion weighted tractography capture the large scale(macroscopic level) functional and structural connectivity between distant regions of the cortex. Using this data from healthy subjects, earlier studies have shown that structure and function are strongly correlated. In this thesis I have investigated the effects of structural perturbations on function and more importantly identified potential underlying neuronal mechanisms that are effected by such structural per-turbations. First we looked at how structure-function correlation of default mode network changes with age to gain insight into processes that might be responsible for cognitive and motor decline with ageing. Next using a bio-physically realistic computational model we investigated how focal lesions across cor-tex effect spontaneous neuronal dynamics. Computational modeling of the spontaneous dynamics over the whole brain provides critical insight into the spatio-temporal organization of brain dynamics at mul-tiple resolutions and their alteration to changes in brain structure (e.g. in diseased states, aging, across individuals). Recent experimental evidence further suggests that the adverse effect of lesions are visible on spontaneous dynamics characterized by changes in resting state functional connectivity and its graph theoretical properties (e.g. modularity). These changes originate from altered neuronal dynamics in individual brain areas that are otherwise poised towards a homeostatic equilibrium to maintain a stable excitatory and inhibitory activity. In this work, we employ a homeostatic inhibitory mechanism, balanc-ing excitation and inhibition in the local brain areas of the entire cortex under neurological impairments like lesions to understand global functional recovery (across brain networks and individuals). Previ-ous computational and empirical studies have demonstrated that the resting state functional connectivity varies primarily due to the location and specific topological characteristics of the lesion. We have shown that local homeostatic balance provides a functional recovery by re-establishing excitation-inhibition balance in all areas that are effected by lesion. We systematically compare the extent of recovery in the primary hub areas (e.g. default mode network (DMN), medial temporal lobe, medial prefrontal cortex) as well as other sensory areas like primary motor area, supplementary motor area, frontoparietal and temporo-parietal networks. Our findings suggest that stability and richness similar to the normal brain dynamics at rest is achievable by re-establishment of local excitation-inhibition balance.

Abstract

Water security, simplistically defined as the capability of a society to ensure that its demands for water are met, is an important aspect which governs not only the well being of individuals in the present, but also acts as a deciding factor for the future prospects and expansion plans of any urban sprawl. The city of Hyderabad is rapidly expanding, resulting in a drastic increase in the population of the metropolis. It is estimated that with this rate of urban growth, the current municipal water distribution system would not be able to fulfil the demands efficiently of the city. Water problem has became the key obstacle for the sustainable development of any city and same goes with Hyderabad. Though the city has a large amount of water bodies within its perimeter , most of them are not being utilized for water distribution. Instead, water is being pumped in the city from rivers far-off. However, the initial and operational cost of such long-distance water projects in terms of resources and energy consumption is several times higher than that of local distribution system. Thus, though this model is fulfilling the current demands of the city, it is not sustainable and cannot be scaled up while maintaining profitability. The solution to this problem needs to rely upon local water bodies in the city. Because of its undulating geography and rocky soil, Hyderabad has a large number of lakes within its vicinity. Several of these are natural, and an equally significant number of reservoirs are man-made. These reservoirs were built with the primary intent of harnessing surface run-off water for local use. However, with rapid urbanization, they succumbed to pollution and encroachment, because of which they are not being utilized currently for supplying water to the city. If these water bodies are included in the citys main water supply, it may prove to be an efficient solution for the cities water issues. The approach described above is more of a proposal, verification of which needs comparative analysis of the citys overall water consumption and the volume of water that can be drawn from the lakes. This poses a major obstacle in verification of the approach described. Calculation of water volume for any irregular shaped water body is a complicated and laborious task. As the circle of study expands from a small locality to a city, state or a country, existing methods to calculate the water volume tend to become highly expensive and thus non scalable. Therefore, a need arises to devise a simpler, yet highly efficient method which can be generically applied to any geographical area for analysing water volume of lakes. The objective of this thesis is to devise a system that relies on local water bodies for supplying water to the city, and in the process propose efficient ways to calculate water volume of any irregularly-shaped water body. In this study, a novel system has been proposed which would utilize local water bodiesfor water distribution in the city. It also presents an efficient approach using open source geospatial technologies (GDAL/OGR library) to determine the volume of water in irregularly shaped lakes with embankment. The approach uses spatial vector data for boundaries, vector data for embankments, and depth data to calculate the amount of water present in any given lake. Algorithms for water volume calculation were written as python modules, leveraging the capabilities offered by the programming language and open source GIS library - the OGR/GDAL library. Calculations for water volume of lakes gave results with considerable accuracy. The error percentage in water volume was found to be mainly dependent on the size of the lake under consideration. It was noticed that for medium-sized lakes with capacity between 50 Mcft. and 70 Mcft, the error percentage was 6.5% and for larger lakes with capacity between 2800 Mcft. to 3500 Mcft, the error percentage was 7.51%. The error percentage was also found to be dependent on the granularity of the grid which was used to model the surface of lake. When applied to all the lakes in Hyderabad, the algorithms gave a gross 9 TMC of water which could be used for supplying to the city. Population data for the city was also gathered from official sources and using the per-capita water consumption of the city, total water demands of the city were estimated. When the water consumption was compared against the volume available for supply, it was found that 67% of the population can be supplied water from local water bodies. This number, though fairly decent, can be improved if the lakes are deepened. By manipulating the embankment depths of lakes used for calculation, this scenario was simulated and it was observed that with increased embankment depths, the water storage capacity of lakes increased to an extent that they could easily meet the demands of 80% of the citys population. This study targets only quantitative analysis

Abstract

Gold nanostructures especially gold nanoparticles and nanoclusters (NCs) have emerged as very prominent metal nanosystems because of their several unique properties. We present here our studies of a physical and a chemical property that are modified enormously by such gold nanostructures, in addition to the synthesis and characterizations of the gold NCs and nanoparticles. The physical and the chemical properties were „surface-enhanced Raman scattering (SERS)’ and the catalytic activities respectively.Currently it is of paramount importance to develop new synthesis strategies that will give us opportunity to observe a system evolving from its few-atom, ultra-small, clusters to nanoparticle size. We have been successful to develop a new one-pot aqueous synthesis protocol where gold nanoclusters starting with size < 1 nm evolved with time to almost ~7.5 nm size nanoparticles. The temporal evolution of nanosystem was studied by various methods like UVVis spectroscopy, electrochemical, MALDI-mass spectrometry, TEM, etc. These systems can be used to study the particle size effects on the catalytic reactions.Raman scattering provides a wealth of information about the chemical structure and dynamics of molecules and materials. However, the limitations of low Raman cross sections of the scatterers can be overcome by the surface-enhanced Raman scattering (SERS) on the roughened metal electrodes and/or colloidal aggregations. Researchers had attempted a variety of substrates for the signal enhancement. Since the magnitude of Raman enhancement depends on a number of factors such as the size, shape, orientation, extent of aggregation, etc. of the nanostructures, it is, therefore, practically very difficult to produce nanostructures that give reproducible and stable SERS. Nanoclusters that have precise compositions, well-defined structures and coordination can be viable SERS substrates. Here we explored the enhanced Raman scattering properties of different sized gold nanoclusters. The gold clusters were found to produce significant static chemical enhancements in Raman signals for small molecules. Various factors such as molecule–cluster interaction energies, charge transfer from the molecule to the metal cluster and total change in the polarizability of the system were calculated and their roles and contributions in the Raman enhancement mechanism were explored. Ligand to cluster charge transfer, HOMO-LUMO gaps of the complexes and the orientation of ligand molecules with respect to gold clusters played significant roles in the chemical enhancement mechanism.We investigated the nanoparticle surface and morphology effects on SERS. We demonstrated that the nature of the surface structure and morphology of the gold nanoparticles strongly influence the Raman signal of a Raman probe. Drastic drop in Raman signal due to the disappearance of sharp edges and tips of the particles directly proved that such sharp structures can enhance the Raman signal to a large extent which is popularly known as “lightening rod effect”. In addition to the individual size and geometry effects, assembled nanosystems can produce extremely large Raman signal enhancements. This effect has been employed for the detection of low concentration analytes placed among assembled nanoparticles. We used sulfur containing amino acid cysteine for linear and controlled assembly of gold nanoparticles of different shapes. Raman enhancement factor was maximal for multi-spiked gold nanoparticles followed by gold nanorod and nanospheres. Finally, we studied the enhanced chemical effects in terms of catalytic activities of gold nanoclusters and nanoparticles. We compared the catalytic effects of the gold nanosystem sizes/concentrations on the reduction reaction of o–nitro aniline to 1,2–benzenediamine. Rate constant value decreased as the size of gold nanocluster increased from <1 nm to ~7.5 nm. Smaller gold nanosystems acted as better catalysts for the reduction reaction compared to the larger ones. This demonstrates that free-standing gold nanoclusters can act as better catalysts than the nanoparticles.